Computational analysis of biological data using manifold and a hyperplane

ABSTRACT

A method of analyzing biological data containing expression values of a plurality of polypeptides in the blood of a subject. The method comprises: calculating a distance between a segment of a curved line and an axis defined by a direction, the distance being calculated at a point over the curved line defined by a coordinate along the direction. The method further comprises correlating the distance to the presence of, absence of, or likelihood that the subject has, a bacterial infection. The coordinate is defined by a combination of the expression values, wherein at least 90% of the segment is between a lower bound line and an upper bound line.

RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.16/998,006, filed on Aug. 20, 2020, which is division of U.S. patentapplication Ser. No. 16/355,984 filed on Mar. 18, 2019, now U.S. Pat.No. 11,081,206 which is a U.S. continuation of U.S. patent applicationSer. No. 15/503,439 filed on Feb. 13, 2017, now U.S. Pat. No.10,303,846, which is a National Phase of PCT Patent Application No.PCT/IL2015/050823 having International Filing Date of Aug. 12, 2015,which claims the benefit of priority under 35 USC § 119(e) of U.S.Provisional Patent Application Nos. 62/105,938 filed on Jan. 21, 2015and 62/037,180 filed on Aug. 14, 2014. The contents of the aboveapplications are all incorporated by reference as if fully set forthherein in their entirety.

SEQUENCE LISTING STATEMENT

The XML file, entitled 92544SequenceListing.xml, created on Jul. 26,2022, comprising 163,109 bytes, submitted concurrently with the filingof this application is incorporated herein by reference. The sequencelisting submitted herewith is identical to the sequence listing formingpart of the international application.

FIELD AND BACKGROUND OF THE INVENTION

The present invention, in some embodiments thereof, relates tocomputational analysis, and, more particularly, but not exclusively, tocomputational analysis of biological data, e.g., for the purpose ofdistinguishing between bacterial infection and non-bacterial disease,and/or between a bacterial infection and viral infection, and/or betweenan infectious and non-infectious disease.

Antibiotics (Abx) are the world's most prescribed class of drugs with a25-30 billion $US global market. Abx are also the world's most misuseddrug with a significant fraction of all drugs (40-70%) being wronglyprescribed (Linder, J. A. and R. S. Stafford 2001; Scott, J. G. and D.Cohen, et al. 2001; Davey, P. and E. Brown, et al. 2006; Cadieux, G. andR. Tamblyn, et al. 2007; Pulcini, C. and E. Cua, et al. 2007), (“CDC—GetSmart: Fast Facts About Antibiotic Resistance” 2011).

One type of Abx misuse is when the drug is administered in case of anon-bacterial disease, such as a viral infection, for which Abx isineffective. For example, according to the USA center for diseasecontrol and prevention CDC, over 60 Million wrong Abx prescriptions aregiven annually to treat flu in the US. The health-care and economicconsequences of the Abx over-prescription include: (i) the cost ofantibiotics that are unnecessarily prescribed globally, estimatedat >$10 billion annually; (ii) side effects resulting from unnecessaryAbx treatment are reducing quality of healthcare, causing complicationsand prolonged hospitalization (e.g. allergic reactions, Abx associateddiarrhea, intestinal yeast etc.) and (iii) the emergence of resistantstrains of bacteria as a result of the overuse (the CDC has declared therise in antibiotic resistance of bacteria as “one of the world's mostpressing health problems in the 21^(st) century” (Arias, C. A. and B. E.Murray 2009; “CDC—About Antimicrobial Resistance” 2011).

Antibiotics under-prescription is not uncommon either. For example up to15% of adult bacterial pneumonia hospitalized patients in the US receivedelayed or no Abx treatment, even though in these instances earlytreatment can save lives and reduce complications (Houck, P. M. and D.W. Bratzler, et al. 2002).

Technologies for infectious disease diagnosis have the potential toreduce the associated health and financial burden associated with Abxmisuse. Ideally, such a technology should: (i) accurately differentiatebetween a bacterial and viral infections; (ii) be rapid (withinminutes); (iii) be able to differentiate between pathogenic andnon-pathogenic bacteria that are part of the body's natural flora; (iv)differentiate between mixed co-infections and pure viral infections and(v) be applicable in cases where the pathogen is inaccessible (e.g.sinusitis, pneumonia, otitis-media, bronchitis, etc).

Current solutions (such as culture, PCR and immunoassays) do not fulfillall these requirements: (i) Some of the assays yield poor diagnosticaccuracy (e.g. low sensitivity or specificity) (Uyeki et al. 2009), andare restricted to a limited set of bacterial or viral strains; (ii) theyoften require hours to days; (iii) they do not distinguish betweenpathogenic and non-pathogenic bacteria (Del Mar, C 1992), thus leadingto false positives; (iv) they often fail to distinguish between a mixedand a pure viral infections and (v) they require direct sampling of theinfection site in which traces of the disease causing agent are searchedfor, thus prohibiting the diagnosis in cases where the pathogen residesin an inaccessible tissue, which is often the case.

Consequentially, there still a diagnostic gap, which in turn often leadsphysicians to either over-prescribe Abx (the “Just-in-case-approach”),or under-prescribe Abx (the “Wait-and-see-approach”) (Little, P. S. andI. Williamson 1994; Little, P. 2005; Spiro, D. M. and K. Y. Tay, et al.2006), both of which have far reaching health and financialconsequences.

Accordingly, a need exists for a rapid method that accuratelydifferentiates between bacterial (including mixed bacterial plus viralinfection), viral and non-bacterial, non-viral disease patients thataddresses these challenges.

WO 2013/117746 teaches signatures and determinants for distinguishingbetween a bacterial and viral infection.

SUMMARY OF THE INVENTION

According to an aspect of some embodiments of the present inventionthere is provided a method of analyzing biological data, the biologicaldata containing expression values of a plurality of polypeptides in theblood of a subject. The method comprises: calculating a distance betweena segment of a curved line and an axis defined by a direction, thedistance being calculated at a point over the curved line defined by acoordinate δ₁ along the direction. The method further comprisescorrelating the distance to the presence of, absence of, or likelihoodthat the subject has a bacterial infection. The coordinate δ₁ is definedby a combination of the expression values, wherein at least 90% of thesegment is between a lower bound line f(δ₁)−ε₀ and an upper bound linef(δ₁)+ε₁, wherein the g(δ₀) equals 1/(1+exp(δ₁)), and wherein each ofthe ε₀ and the ε₁ is less than 0.5.

According to some embodiments of the invention the method comprisesobtaining the likelihood based on the distance, comparing the likelihoodto a predetermined threshold, and, treating the subject for thebacterial infection when the likelihood is above the predeterminedthreshold.

According to an aspect of some embodiments of the present inventionthere is provided a method of analyzing biological data, the biologicaldata containing expression values of a plurality of polypeptides in theblood of a subject. The method comprises: calculating a distance betweena segment of a curved line and an axis defined by a direction, thedistance being calculated at a point over the curved line defined by acoordinate δ₀ along the direction. The method further comprisescorrelating the distance to the presence of, absence of, or likelihoodthat the subject has a viral infection. The coordinate δ₀ is defined bya combination of the expression values, wherein at least 90% of thesegment is between a lower bound line g(δ₀)−ε₀ and an upper bound lineg(δ₀)+ε₁, wherein the f(δ₀) equals 1/(1+exp(δ₀)), and wherein each ofthe ε₀ and the ε₁ is less than 0.5.

According to some embodiments of the invention the method comprisesobtaining the likelihood based on the distance, comparing the likelihoodto a predetermined threshold, and, treating the subject for the viralinfection when the likelihood is above the predetermined threshold.

According to some embodiments of the invention the combination of theexpression values comprises a linear combination of the expressionvalues.

According to some embodiments of the invention the combination of theexpression values includes at least one nonlinear term corresponding toat least one of the expression values.

According to an aspect of some embodiments of the present inventionthere is provided a method of analyzing biological data, the biologicaldata containing expression values of a plurality of polypeptides in theblood of a subject. The method comprises: calculating a first distancebetween a segment of a curved surface and a plane defined by a firstdirection and a second direction. The first distance being calculated ata point over the surface defined by first coordinate δ₀ along the firstdirection and a second coordinate δ₁ along the second direction. Themethod further comprises correlating the first distance to the presenceof, absence of, or likelihood that the subject has a bacterialinfection. Each of the coordinates is defined by a different combinationof the expression values, wherein at least 90% of the segment is betweena lower bound surface f(δ₀,δ₁)−ε₀ and an upper bound surfacef(δ₀,δ₁)+ε₁, wherein the f(δ₀,δ₁) equals exp(δ₁)/(1+exp(δ₀)+exp(δ₁)),and wherein each of the ε₀ and the ε₁ is less than 0.5.

According to some embodiments of the invention for at least one of thecoordinates, the combination of the expression values comprises a linearcombination of the expression values.

According to some embodiments of the invention for at least one of thecoordinates, the combination of the expression values includes at leastone nonlinear term corresponding to at least one of the expressionvalues.

According to some embodiments of the invention the method comprisesobtaining the likelihood based on the first distance, comparing thelikelihood to a predetermined threshold, and, treating the subject forthe bacterial infection when the likelihood is above the predeterminedthreshold.

According to some embodiments of the invention the method comprisescalculating a second distance between a segment of second curved surfaceand the plane; and correlating the second distance to the presence of,absence of, or likelihood that the subject has a viral infection.According to some embodiments of the invention at least 90% of thesegment of the second surface is between a second lower bound surfaceg(δ₀,δ₁)−ε₂ and a second upper bound surface g(δ₀,δ₁)+ε₃, wherein theg(δ₀,δ₁) equals exp(δ₀)/(1+exp(δ₀)+exp(δ₁)), and wherein each of the ε₂and the ε₃ is less than 0.5.

According to some embodiments of the invention the method comprisesobtaining the likelihood based on the second distance, comparing thelikelihood to a second predetermined threshold, and, treating thesubject for the viral infection when the likelihood is above the secondpredetermined threshold.

According to some embodiments of the invention the method comprisesobtaining the likelihood that the subject has a bacterial infectionbased on the distance, obtaining the likelihood that the subject has aviral infection based on the second distance, comparing each of thelikelihoods to a respective predetermined threshold, and, when each ofthe likelihoods is below the respective predetermined threshold, thendetermining that the patient is likely to have a non-infectious disease.

According to an aspect of some embodiments of the present inventionthere is provided a method of analyzing biological data, the biologicaldata containing expression values of a plurality of polypeptides in theblood of a subject. The method comprises: calculating a distance betweena segment of a curved surface and a plane defined by a first directionand a second direction. The distance is calculated at a point over thesurface defined by first coordinate δ₀ along the first direction and asecond coordinate δ₁ along the second direction. The method comprisescorrelating the distance to the presence of, absence of, or likelihoodthat the subject has, a viral infection; wherein each of the coordinatesis defined by a different combination of the expression values, whereinat least 90% of the segment is between a lower bound surface g(δ₀,δ₁)−ε₀and an upper bound surface g(δ₀,δ₁)+δ₁, wherein the g(δ₀,δ₁) equalsexp(δ₀)/(1+exp(δ₀)+exp(δ₁)), and wherein each of the ε₀ and the ε₁ isless than 0.5.

According to some embodiments of the invention each of the plurality ofpolypeptides is selected from the group consisting of CRP, IP-10, TRAIL,IL1ra, PCT and SAA.

According to some embodiments of the invention the plurality ofpolypeptides comprises at least three polypeptides.

According to some embodiments of the invention the plurality ofpolypeptides comprises at least three polypeptides selected from thegroup consisting of CRP, IP-10, TRAIL, IL1ra, PCT and SAA.

According to some embodiments of the invention the plurality ofpolypeptides comprises at least CRP and TRAIL.

According to some embodiments of the invention the plurality ofpolypeptides comprises at least CRP, TRAIL and IP-10.

According to some embodiments of the invention the method comprisesgenerating an output of the likelihood, the output is presented as text.

According to some embodiments of the invention the method comprisesgenerating an output of the likelihood, the output is presentedgraphically.

According to some embodiments of the invention the method comprisesgenerating an output of the likelihood, the output is presented using acolor index.

According to some embodiments of the invention the blood sample is wholeblood.

According to some embodiments of the invention the blood sample is afraction of whole blood.

According to some embodiments of the invention the blood fractioncomprises serum or plasma.

According to some embodiments of the invention the method comprisesdetermining the expression values, and wherein at least one of theexpression values is determined electrophoretically or immunochemically.

According to some embodiments of the invention the immunochemicaldetermination is effected by flow cytometry, radioimmunoassay,immunofluorescence or by an enzyme-linked immunosorbent assay.

According to some embodiments of the invention the calculating and thecorrelating is executed by a computer remote from the subject.

According to some embodiments of the invention the calculating and thecorrelating is executed by a computer near the subject.

According to some embodiments of the invention the calculating and thecorrelating is executed by a cloud computing resource of a cloudcomputing facility.

According to some embodiments of the invention the expression values aremeasured by a measuring system performing at least one automated assayselected from the group consisting of an automated ELISA, an automatedimmunoassay, and an automated functional assay, and the method comprisesreceiving said the biological data from said measuring system.

According to some embodiments of the invention the receiving is over aninternet network via a network interface.

According to an aspect of some embodiments of the present inventionthere is provided a computer-implemented method for analyzing biologicaldata. The method comprises: displaying on a display device a graphicaluser interface (GUI) having a calculation activation control; receivingexpression values of polypeptides in the blood of a subject;responsively to an activation of the control by a user, automaticallycalculating a score based on the expression values; generating on theGUI a graphical scale having a first end identified as corresponding toa viral infection of the subject, and a second end identified ascorresponding to a bacterial infection the subject; and generating amark on the scale at a location corresponding to the score.

According to some embodiments of the invention the expression values arereceived by communicating with an external machine that measures theexpression values. According to some embodiments of the invention theGUI comprises a communication control, wherein the communication withthe external machine is in response to an activation of thecommunication control by the user.

According to some embodiments of the invention the GUI comprises aplurality of an expression value input fields, wherein the expressionvalues are received via the input fields.

According to some embodiments of the invention the score is a likelihoodthat the subject has bacterial infection. According to some embodimentsof the invention the score is a likelihood that the subject has viralinfection.

According to an aspect of some embodiments of the present inventionthere is provided a computer software product, comprising acomputer-readable medium in which program instructions are stored, whichinstructions, when read by a hardware processor, cause the hardwareprocessor to receive expression values of a plurality of polypeptides inthe blood of a subject who has an unknown disease, and to execute themethod as delineated above and optionally as further detailed below.

According to an aspect of some embodiments of the present inventionthere is provided a system for analyzing biological data. The systemcomprises: a user interface configured to receive expression values of aplurality of polypeptides in the blood of a subject who has an unknowndisease; and a hardware processor having a computer-readable mediumstoring the computer software product.

According to an aspect of some embodiments of the present inventionthere is provided a system for analyzing biological data. The systemcomprises: a first compartment configured to measure expression valuesof a plurality of polypeptides in the blood of a subject who has anunknown disease; a second compartment comprising a hardware processorhaving a computer-readable storing the computer software product.

According to some embodiments of the invention the first compartment,the second compartment and the display are mounted on or integrated witha body of a hand-held device.

According to an aspect of some embodiments of the present inventionthere is provided a method of analyzing a dataset. The method comprises:(a) accessing a dataset comprising classification groups based onexpression values of a plurality of polypeptides in the blood of asubject who has an unknown disease in blood samples of multiplesubjects, wherein the classification groups comprise a bacterialinfection, a viral infection and a non-viral, non bacterial disease; and(b) analyzing the classification groups to provide at least a firstprobabilistic classification function f(δ₀,δ₁) representing thelikelihood that a particular subject has a bacterial infection, thefirst classification function being a function of a first coordinate δ₀and a second coordinate δ₁, and wherein each of the coordinates isdefined by a different combination of the expression values.

According to some embodiments of the invention the method furthercomprising calculating a second classification function g(δ₀,δ₁)representing the likelihood that a particular subject has a viralinfection, the second classification function being also a function ofthe first and the second coordinates.

According to some embodiments of the invention the method comprisescalculating a third classification function h(δ₀,δ₁) representing thelikelihood that a particular subject has a non-viral, non bacterialdisease, the third classification function being also a function of thefirst and the second coordinates.

According to some embodiments of the invention, for at least one of thecoordinates, the combination of the expression values comprises a linearcombination of the expression values.

According to some embodiments of the invention for at least one of thecoordinates, the combination of the expression values includes at leastone nonlinear term corresponding to at least one of the expressionvalues.

According to some embodiments of the invention the method comprisesgenerating an output of the analyzing.

According to some embodiments of the invention the dataset comprises oneor more multidimensional entries.

According to some embodiments of the invention the method wherein eachentry in the dataset comprises at least one clinical parameter of therespective subject.

According to some embodiments of the invention the method wherein theclinical parameter is selected from the group consisting of a sex, anage, a temperature, a time from symptoms onset and a weight.

According to some embodiments of the invention the analysis comprisesmachine learning.

According to some embodiments of the invention the machine learningcomprises a supervised machine learning.

According to some embodiments of the invention the machine learningcomprises at least one procedure selected from the group consisting ofclustering, support vector machine, linear modeling, k-nearest neighborsanalysis, decision tree learning, ensemble learning procedure, neuralnetworks, probabilistic model, graphical model, Bayesian network,logistic regression and association rule learning.

According to some embodiments of the invention the method wherein themachine learning is selected from the group consisting of support vectormachine, neural networks and logistic regression.

According to some embodiments of the invention the blood sample is wholeblood.

According to some embodiments of the invention the blood sample is afraction of whole blood.

According to some embodiments of the invention the blood fractioncomprises serum or plasma.

According to some embodiments of the invention the expression value isdetermined electrophoretically or immunochemically.

According to some embodiments of the invention the immunochemicaldetermination is effected by flow cytometry, radioimmunoassay,immunofluorescence or by an enzyme-linked immunosorbent assay.

According to an aspect of some embodiments of the present inventionthere is provided a method of predicting a prognosis for a disease. Themethod comprises measuring the TRAIL protein serum level in subjecthaving the disease, wherein when the TRAIL level is below apredetermined level, the prognosis is poorer than for a subject having adisease having a TRAIL protein serum level above the predeterminedlevel.

According to some embodiments of the invention the method wherein thedisease is an infectious disease.

According to some embodiments of the invention the method wherein thedisease is not an infectious disease.

According to an aspect of some embodiments of the present inventionthere is provided a method of determining a treatment course for adisease in a subject. The method comprises measuring the TRAIL proteinserum level in the subject, wherein when the TRAIL level is below apredetermined level, the subject is treated with a treatment of lastresort.

According to some embodiments of the invention the predetermined levelis below 20 pg/ml.

According to an aspect of some embodiments of the present inventionthere is provided a method of determining an infection type in a femalesubject of fertility age.

The method comprises comparing the TRAIL protein serum level in thesubject to a predetermined threshold, the predetermined thresholdcorresponding to the TRAIL protein serum level of a healthy femalesubject of fertility age, or a group of healthy female subjects offertility age, wherein a difference between the TRAIL protein serumlevel and the predetermined threshold is indicative of an infectiontype.

According to an aspect of some embodiments of the present inventionthere is provided a method of determining an infection type in a malesubject of fertility age.

The method comprises comparing the TRAIL protein serum level in thesubject to a predetermined threshold, the predetermined thresholdcorresponding to the TRAIL protein serum level of a healthy male subjectof fertility age, or a group of healthy male subjects of fertility age,wherein a difference between the TRAIL protein serum level and thepredetermined threshold is indicative of an infection type.

According to some embodiments of the invention when the TRAIL proteinserum level is above the predetermined threshold, the infection type isviral.

According to some embodiments of the invention when the TRAIL proteinserum level is above the predetermined threshold, the infection type isnot bacterial.

According to some embodiments of the invention when the TRAIL proteinserum level is below the predetermined threshold, the infection type isbacterial.

According to some embodiments of the invention when the TRAIL proteinserum level is below the predetermined threshold, the infection type isnot viral.

Unless otherwise defined, all technical and/or scientific terms usedherein have the same meaning as commonly understood by one of ordinaryskill in the art to which the invention pertains. Although methods andmaterials similar or equivalent to those described herein can be used inthe practice or testing of embodiments of the invention, exemplarymethods and/or materials are described below. In case of conflict, thepatent specification, including definitions, will control. In addition,the materials, methods, and examples are illustrative only and are notintended to be necessarily limiting.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

Some embodiments of the invention are herein described, by way ofexample only, with reference to the accompanying drawings. With specificreference now to the drawings in detail, it is stressed that theparticulars shown are by way of example and for purposes of illustrativediscussion of embodiments of the invention. In this regard, thedescription taken with the drawings makes apparent to those skilled inthe art how embodiments of the invention may be practiced.

In the drawings:

FIGS. 1A-1B. Study workflow. (A) An overview of the study workflow.n_(Bacterial), n_(Viral) and n_(Control) represent the number ofbacterial (including mixed bacterial plus viral co-infections), viraland control (with no apparent infectious disease) cases, respectively.(B) Proteins discovery and validation process.

FIGS. 2A-2C. The proteins TRAIL, IP-10 and CRP are differentiallyexpressed in bacterial, viral and non-infectious patients. Box plots forTRAIL (A), IP-10 (B), and CRP (C), measured over the Majority cohort(n=765) are presented. Boxed line and circle correspond to group medianand average respectively; t-test p-values between bacterial and viralgroups and between infectious (bacterial and viral) vs. non-infectious(including healthy subjects) are depicted.

FIGS. 3A-3B. Comparison of the signature to lab parameters and proteinbiomarkers for diagnosing bacterial vs. viral patients. (A) Performanceof clinical and lab parameters as well as the best performing pair (ANCand Lym %), triplet (ANC, Lym % and Pulse), and quadruplets (ANC, Lym %,Pulse, Mono %) of parameters, the values of which were combined using alogistic regression. Comparison was done on the Majority cohort(bacterial and viral patients, n=653), apart from pulse (recorded in 292bacterial and 326 viral patients), and respiratory rate (recorded in 292bacterial and 326 viral patients). The signature performed significantlybetter (P<10⁻¹⁵) than the optimal quadruplet. (B) The signatureperformed significantly better (P<10⁻⁸) than biomarkers with awell-established role in the host response to infections. For each ofthe select biomarkers, analysis was performed in a subgroup of theMajority cohort (43≤n≤154 for each analysis, a convenience sample, ndepended on the strength of the signal). Error bars represent 95% CI.

FIG. 4 . Signature performance is robust across different patientsubgroups. Signature AUC in subgroups of the Majority cohort (bacterialand viral) are depicted. Square size is proportional to number ofpatients and error bars represent 95% CI. In the Pathogens analysis,each virus was compared to bacteria affecting the same physiologicalsystem, indicated in brackets. R—respiratory, S—systemic, C—centralnervous system, G—gastrointestinal, U—urinary, K—skin. Only pathogensdetected in more than 5 patients are presented. For subgroup definitionssee Table 1 in Example 1.

FIG. 5 . Calibration plot of the MLR model. In the top panel patientswere grouped into 10 bins based on their predicted probabilities of abacterial infection (x-axis), and compared to the observed fraction ofbacterial infections within each bin (y-axis). Dashed line is a movingaverage (of size 5 bins). The bottom panel shows the distribution ofpredicted probabilities for bacterial (upper bars) and viral (lowerbars).

FIGS. 6A-6B. Age distribution of the diagnosed patients. A. The entirestudy population (n=794); B. Pediatric patients only (n=445).

FIGS. 7A-7B. Distribution of detected pathogens in diagnosed patients(n=794). A. Distribution of detected pathogens by pathogenic subgroups;B. Distribution of detected pathogens by strain (strains detectedfrom >1% of patients are presented). Distribution represents % ofpositive detections in patients with diagnosed infectious disease.

FIG. 8 . Distribution of involved physiologic systems in patientsdiagnosed with an infectious disease (n=673).

FIGS. 9A-9B. Distribution of clinical syndromes (all diagnosed patients,n=794). A. Major clinical syndromes; B. Specific clinical syndromes.

FIG. 10 . Distribution of maximal body temperatures (n=794).

FIG. 11 . Distribution of time from initiation of symptoms (n=794).N/A—healthy controls or patients for which data was not obtained.

FIGS. 12A-12B. Comorbidities-related characterization of the patientpopulation. A. Distribution of comorbidities (all chronically illpatients, n=305); B. Distribution of chronic medications (allchronically ill patients, n=305). Of note, some of the patientspresented with several chronic diseases, and treated with severalchronic medications.

FIG. 13 . Distribution of recruitment sites (diagnosed patients, n=794).

FIGS. 14A-14B. Extrapolated PPV and NPV values for the signature as afunction of the prevalence of bacterial infections, A. Unanimous(bacterial, viral) cohort (n=527), B. Majority (bacterial, viral) cohort(n=653).

FIGS. 15A-15E. Scatter plots of clinical parameters and laboratorymeasurements in bacterial, viral, and non-infectious patients (asindicated) in the Majority (bacterial, viral, non-infectious) cohort(n=765). Boxed line and circle correspond to group median and averagerespectively. T-test p-values between bacterial and viral groups andbetween infectious (bacterial and viral) vs. non-infectious (includinghealthy subjects) are depicted.

FIGS. 16A-16B. Comparison of the performance of the signature and PCTusing different cutoffs. A. Performance measured in 76 patients from theUnanimous (bacterial, viral) cohort; B. Performance measured in 101patients from the Majority (bacterial, viral) cohort. Error barsrepresent 95% CI. Signature sensitivity (left) and specificity (right)were calculated after filtering out 14% of the patients with a marginalimmune response.

FIGS. 17A-17B. Comparison of the performance of the signature and CRPusing different cutoffs. A. Performance measured in the Unanimous(bacterial, viral) cohort (n=527); B. Performance measured in theMajority (bacterial, viral) cohort (n=653). Error bars represent 95% CI.Signature sensitivity (left) and specificity (right) were calculatedafter filtering out 14% of the patients with a marginal immune response.

FIGS. 18A-18H. Scatter plots of levels of selected protein biomarkers(arbitrary units) in bacterial and viral patients. Boxed line and circlecorrespond to group median and average respectively. T-test p-valuesbetween bacterial and viral groups are depicted.

FIGS. 19A-19B. The clinical accuracy of the signature is robust toreduction in the technical accuracy of protein measurements. (A) TheAUCs of the signature distinguishing bacterial from viral infection areestimated using a grayscale map as a function of CVs (std/mean) of TRAIL(y-axis) and CRP (x-axis) measurement. (B) AUC values on the diagonal ofFIG. 19A a presented such that CV of TRAIL and CRP are equal.

FIG. 20 is a 3-dimensional visualization of bacterial (‘+’), viral (‘o’)and non-infectious (‘{circumflex over ( )}’) patients. Differentpatients types are mapped to distinct regions in the CRP (μg/ml), TRAILand IP-10 (pg/ml) concentration map.

FIGS. 21A-21C. Probability of viral (A) bacterial or mixed (B) andnon-infectious or healthy (C) as a function of TRAIL (y-axis), CRP(x-axis), and IP-10 concentrations, as obtained according to someembodiments of the present invention for IP-10 ranging from 0 to 100.

FIGS. 22A-22C. Probability of viral (A) bacterial or mixed (B) andnon-infectious or healthy (C) as a function of TRAIL (y-axis), CRP(x-axis), and IP-10 concentrations, as obtained according to someembodiments of the present invention for IP-10 ranging from 100 to 200.

FIGS. 23A-23C. Probability of viral (A) bacterial or mixed (B) andnon-infectious or healthy (C) as a function of TRAIL (y-axis), CRP(x-axis), and IP-10 concentrations, as obtained according to someembodiments of the present invention for IP-10 ranging from 200 to 300.

FIGS. 24A-24C. Probability of viral (A) bacterial or mixed (B) andnon-infectious or healthy (C) as a function of TRAIL (y-axis), CRP(x-axis), and IP-10 concentrations, as obtained according to someembodiments of the present invention for IP-10 ranging from 300 to 400.

FIGS. 25A-25C. Probability of viral (A) bacterial or mixed (B) andnon-infectious or healthy (C) as a function of TRAIL (y-axis), CRP(x-axis), and IP-10 concentrations, as obtained according to someembodiments of the present invention for IP-10 ranging from 400 to 500.

FIGS. 26A-26C. Probability of viral (A) bacterial or mixed (B) andnon-infectious or healthy (C) as a function of TRAIL (y-axis), CRP(x-axis), and IP-10 concentrations, as obtained according to someembodiments of the present invention for IP-10 ranging from 500 to 1000.

FIGS. 27A-27C. Probability of viral (A) bacterial or mixed (B) andnon-infectious or healthy (C) as a function of TRAIL (y-axis), CRP(x-axis), and IP-10 concentrations, as obtained according to someembodiments of the present invention for IP-10 ranging from 1000 to2000.

FIGS. 28A-28C. Probability of viral (A) bacterial or mixed (B) andnon-infectious or healthy (C) as a function of TRAIL (y-axis), CRP(x-axis), and IP-10 concentrations, as obtained according to someembodiments of the present invention for IP-10 which is 2000 or more.

FIGS. 29A-29F illustrate exemplary outputs of the method fordistinguishing between bacterial and non-bacterial infection accordingto an embodiment of the present invention.

FIGS. 30A-30B are graphs illustrating the correlation between the rapidand slow protocol for measurement of TRAIL (FIG. 30A) and IP-10 (FIG.30B).

FIG. 31 is a flowchart diagram of a method suitable for analyzingbiological data obtained from a subject, according to various exemplaryembodiments of the present invention.

FIGS. 32A-32B are schematic illustrations describing a procedure forcalculating a distance of a surface from a plane according to someembodiments of the present invention.

FIGS. 33A-33D are schematic illustrations describing a procedure forobtaining the smooth version of a segment of a surface, according tosome embodiments of the present invention.

FIG. 34 is a schematic illustration of a block diagram of a system foranalyzing biological data, according to some embodiments of the presentinvention.

FIGS. 35A-35D are contour plots describing the probability of bacterial(FIG. 35A), viral (FIG. 35B), non-bacterial (FIG. 35C), andnon-infectious (FIG. 35D) etiologies as a function of the coordinates δ₀and δ₁. The probability values range between 0% (black) to 100% (white).

FIGS. 36A-36B. Low TRAIL levels are indicative or poor patient prognosisand outcome and high disease severity. (A) TRAIL concentrations in theserum of patients that were admitted to the ICU compared to all otherpatients (with infectious or non-infectious etiology). (B) TRAILconcentrations in the serum of pediatric patients that were admitted tothe ICU or died compared to all other patients with infectious ornon-infectious etiology.

FIGS. 37A-37B are graphs illustrating the difference in TRAILconcentrations in males and females of fertility age.

FIGS. 38A-38E are screenshots of a graphical user interface (GUI)suitable for receiving user input in a computer-implemented method foranalyzing biological data according to some embodiments of the presentinvention.

FIGS. 39A and 39B are schematic illustrations of a block diagram of asystem for analyzing biological data, in embodiments of the invention inwhich the system comprises a network interface (FIG. 39A) and a userinterface (FIG. 39B).

DESCRIPTION OF SPECIFIC EMBODIMENTS OF THE INVENTION

The present invention, in some embodiments thereof, relates tocomputational analysis, and, more particularly, but not exclusively, tocomputational analysis of biological data, e.g., for the purpose ofdistinguishing between bacterial infection and non-bacterial disease,and/or between a bacterial infection and viral infection, and/or betweenan infectious and non-infectious disease.

Before explaining at least one embodiment of the invention in detail, itis to be understood that the invention is not necessarily limited in itsapplication to the details set forth in the following description orexemplified by the Examples. The invention is capable of otherembodiments or of being practiced or carried out in various ways.

Different infectious agents have unique molecular patterns that can beidentified and targeted by the immune system. Pathogen-associatedmolecular patterns (PAMPs) are an example of such molecules that areassociated with different groups of pathogens and may be recognized bycells of the innate immune system using Toll-like receptors (TLRs) andother pattern recognition receptors (e.g. NOD proteins).

These patterns may vary considerably between different classes ofpathogens and thus elicit different immune responses. For example, TLR-4can recognize lipopolysaccharide, a constituent of gram negativebacteria, as well as lipoteichoic acids, constituent of gram positivebacteria, hence promoting an anti-microbial response of the immunesystem. TLR-3 can recognize single stranded RNA (often indicative of aviral infection) and thus prompt the appropriate anti-viral response. Bydistinguishing between different classes of pathogens (e.g. bacterialversus viral) the immune system can mount the appropriate defense.

In the past few decades, several host markers have been identified thatcan be used for differential diagnosis of infection source in variousindications. By measuring markers derived from the host rather than thepathogen, it is possible to minimize “false-positive” diagnoses due tonon-pathogenic strains of bacteria that are part of the body's naturalflora. One example is Procalcitonin (PCT), a precursor of the hormonecalcitonin produced by the C-cells of the thyroid gland. PCT levels inthe blood stream of healthy individuals is hardly detectable (in thepg/ml range) but it might increase dramatically, as a result of a severeinfection with levels rising up to 100 ng/ml. PCT is heavily used todiagnose patients with systemic infection, sepsis, with sensitivity of76% and specificity of 70%. However, studies that tested the diagnosticvalue of PCT in other non-systemic infection such as pneumonia or upperrespiratory tract infections found it to be limited, especially whenused in isolation.

The present inventors previously identified novel sets of biomarkerswhose pattern of expression significantly correlates with infectiontype—as documented in International Patent Application WO2011132086 andWO2013/117746, both of which are incorporated herein by reference.

The present invention, in some embodiments thereof, is based on the useof signature of polypeptides for the diagnosis of bacterial infections,viral infections and non-bacterial, non-viral diseases. The methods ofthe present embodiments employ pattern recognition algorithms for theidentification of the type of infection a subject is suffering from,which in turn allows for the selection of an appropriate treatmentregimen. Various embodiments of the invention address limitations ofcurrent diagnostic solutions by: (i) allowing accurate diagnostics on abroad range of pathogens; (ii) enabling rapid diagnosis (withinminutes); (iii) insensitivity to the presence of non-pathogenic bacteriaand viruses (thus reducing the problem of false-positive); and (iv)eliminating the need for direct sampling of the pathogen, thus enablingdiagnosis of inaccessible infections. Thus, some methods of theinvention allow for the selection of subjects for whom antibiotictreatment is desired and prevent unnecessary antibiotic treatment ofsubjects having only a viral infection or a non-infectious disease. Somemethods of the invention also allow for the selection of subjects forwhom anti-viral treatment is advantageous.

To corroborate the findings in International Patent ApplicationWO2013/117746, the present inventors have now increased the number ofpatients taking part in a multi-center clinical trial, enrolling 1002hospital patients with different types of established infections as wellas controls (patients with established non-viral/non-bacterial diseaseand healthy individuals).

Seeking to improve the level of accuracy and sensitivity of thepreviously described methods, the present inventors have now used atrinary classifier, which classifies patients (those having anestablished disease type) into one of three classes: bacterialinfection, viral infection and non-bacterial, non-viral disease.Comparing the levels of a combination of polypeptides of a test subjectwith the expression patterns obtained in the study yielded superiorresults in terms of sensitivity and specificity compared to a binaryclassifier as summarized in Example 3 and Tables 9-12.

In the context of the present invention, the following abbreviations maybe used: ANC=Absolute neutrophil count; ANN=Artificial neural networks;AUC=Area under the receiver operating curve; BP=Bordetella pertussis;CHF=Congestive heart failure; CI=Confidence interval; CID=Congenitalimmune deficiency; CLL=Chronic lymphocytic leukemia;CMV=Cytomegalovirus; CNS=Central nervous system; COPD=Chronicobstructive pulmonary disease; CP=Chlamydophila pneumonia;CRP=C-reactive protein; CSF=Cerebrospinal fluid; CV=Coefficient ofvariation; DOR=Diagnostic odds ratio; EBV=Epstein bar virus;eCRF=Electronic case report form; ED=Emergency department,ELISA=Enzyme-linked immunosorbent assay; FDR=False discovery rate;FMF=Familial Mediterranean fever; G-CSF=Granulocyte colony-stimulatingfactor; GM-CSF=Granulocyte-macrophage colony-stimulating factor;HBV=Hepatitis B virus; HCV=Hepatitis C virus; HI=Haemophilus influenza;HIV=Human immunodeficiency virus; IDE=Infectious disease experts;IL=Interleukin; IRB=institutional review board; IVIG=Intravenousimmunoglobulin; KNN=K-nearest neighbors; LP=Legionella pneumophila;LR+=Positive likelihood ratio; LR−=Negative likelihood ratio; LRTI=Lowerrespiratory tract infections; mAb=Monoclonal antibodies; MDD=Minimumdetectable dose; MDS=Myelodysplastic syndrome; MP=Mycoplasma pneumonia;MPD=Myeloproliferative disease; NPV=Negative predictive value;PCT=Procalcitonin; PED=Pediatric emergency department; PPV=Positivepredictive value; QA=Quality assurance; RSV=Respiratory syncytial virus;RV=Rhinovirus; SIRS=systemic inflammatory syndrome; SP=Streptococcuspneumonia; STARD=Standards for Reporting of Diagnostic Accuracy;SVM=Support vector machine; TNF=Tumor necrosis factor; URTI=Upperrespiratory tract infection; UTI=Urinary tract infection; WBC=Whiteblood cell; WS=Wilcoxon rank-sum.

In the context of the present invention, the following statistical termsmay be used:

“TP” is true positive, means positive test result that accuratelyreflects the tested-for activity. For example in the context of thepresent invention a TP, is for example but not limited to, trulyclassifying a bacterial infection as such.

“TN” is true negative, means negative test result that accuratelyreflects the tested-for activity. For example in the context of thepresent invention a TN, is for example but not limited to, trulyclassifying a viral infection as such.

“FN” is false negative, means a result that appears negative but failsto reveal a situation. For example in the context of the presentinvention a FN, is for example but not limited to, falsely classifying abacterial infection as a viral infection.

“FP” is false positive, means test result that is erroneously classifiedin a positive category. For example in the context of the presentinvention a FP, is for example but not limited to, falsely classifying aviral infection as a bacterial infection.

“Sensitivity” is calculated by TP/(TP+FN) or the true positive fractionof disease subjects.

“Specificity” is calculated by TN/(TN+FP) or the true negative fractionof non-disease or normal subjects.

“Total accuracy” is calculated by (TN+TP)/(TN+FP+TP+FN).

“Positive predictive value” or “PPV” is calculated by TP/(TP+FP) or thetrue positive fraction of all positive test results. It is inherentlyimpacted by the prevalence of the disease and pre-test probability ofthe population intended to be tested.

“Negative predictive value” or “NPV” is calculated by TN/(TN+FN) or thetrue negative fraction of all negative test results. It also isinherently impacted by the prevalence of the disease and pre-testprobability of the population intended to be tested. See, e.g.,O'Marcaigh A S, Jacobson R M, “Estimating The Predictive Value Of ADiagnostic Test, How To Prevent Misleading Or Confusing Results,” Clin.Ped. 1993, 32(8): 485-491, which discusses specificity, sensitivity, andpositive and negative predictive values of a test, e.g., a clinicaldiagnostic test.

“MCC” (Mathews Correlation coefficient) is calculated as follows:MCC=(TP*TN−FP*FN)/{(TP+FN)*(TP+FP)*(TN+FP)*(TN+FN)}{circumflex over( )}0.5

where TP, FP, TN, FN are true-positives, false-positives,true-negatives, and false-negatives, respectively. Note that MCC valuesrange between −1 to +1, indicating completely wrong and perfectclassification, respectively. An MCC of 0 indicates randomclassification. MCC has been shown to be a useful for combiningsensitivity and specificity into a single metric (Baldi, Brunak et al.2000). It is also useful for measuring and optimizing classificationaccuracy in cases of unbalanced class sizes (Baldi, Brunak et al. 2000).

“Accuracy” refers to the degree of conformity of a measured orcalculated quantity (a test reported value) to its actual (or true)value. Clinical accuracy relates to the proportion of true outcomes(true positives (TP) or true negatives (TN) versus misclassifiedoutcomes (false positives (FP) or false negatives (FN)), and may bestated as a sensitivity, specificity, positive predictive values (PPV)or negative predictive values (NPV), Mathews correlation coefficient(MCC), or as a likelihood, odds ratio, Receiver Operating Characteristic(ROC) curve, Area Under the Curve (AUC) among other measures.

“Analytical accuracy” refers to the reproducibility and predictabilityof the measurement process itself, and may be summarized in suchmeasurements as coefficients of variation (CV), Pearson correlation, andtests of concordance and calibration of the same samples or controlswith different times, users, equipment and/or reagents. These and otherconsiderations in evaluating new biomarkers are also summarized inVasan, 2006.

“Performance” is a term that relates to the overall usefulness andquality of a diagnostic or prognostic test, including, among others,clinical and analytical accuracy, other analytical and processcharacteristics, such as use characteristics (e.g., stability, ease ofuse), health economic value, and relative costs of components of thetest. Any of these factors may be the source of superior performance andthus usefulness of the test, and may be measured by appropriate“performance metrics,” such as AUC and MCC, time to result, shelf life,etc. as relevant.

By “statistically significant”, it is meant that the alteration isgreater than what might be expected to happen by chance alone (whichcould be a “false positive”). Statistical significance can be determinedby any method known in the art. Commonly used measures of significanceinclude the p-value, which presents the probability of obtaining aresult at least as extreme as a given data point, assuming the datapoint was the result of chance alone. A result is often consideredhighly significant at a p-value of 0.05 or less.

Aspects of the invention will now be described in detail.

FIG. 31 is a flowchart diagram of a method suitable for analyzingbiological data obtained from a subject, according to various exemplaryembodiments of the present invention. It is to be understood that,unless otherwise defined, the operations described hereinbelow can beexecuted either contemporaneously or sequentially in many combinationsor orders of execution. Specifically, the ordering of the flowchartdiagrams is not to be considered as limiting. For example, two or moreoperations, appearing in the following description or in the flowchartdiagrams in a particular order, can be executed in a different order(e.g., a reverse order) or substantially contemporaneously.Additionally, several operations described below are optional and maynot be executed.

In some embodiments of the present invention the subject has beenpreviously treated with an antibiotic, and in some embodiments of thepresent invention the subject has not been previously treated with anantibiotic.

Any of the methods described herein can be embodied in many forms. Forexample, it can be embodied in on a tangible medium such as a computerfor performing the method operations. It can be embodied on a computerreadable medium, comprising computer readable instructions for carryingout the method operations. It can also be embodied in electronic devicehaving digital computer capabilities arranged to run the computerprogram on the tangible medium or execute the instruction on a computerreadable medium.

Computer programs implementing the method of the present embodiments cancommonly be distributed to users on a distribution medium such as, butnot limited to, CD-ROMs or flash memory media. From the distributionmedium, the computer programs can be copied to a hard disk or a similarintermediate storage medium. In some embodiments of the presentinvention, computer programs implementing the method of the presentembodiments can be distributed to users by allowing the user to downloadthe programs from a remote location, via a communication network, e.g.,the internet. The computer programs can be run by loading the computerinstructions either from their distribution medium or their intermediatestorage medium into the execution memory of the computer, configuringthe computer to act in accordance with the method of this invention. Allthese operations are well-known to those skilled in the art of computersystems.

The computational operations of the method of the present embodimentscan be executed by a computer, either remote from the subject or nearthe subject. When the computer is remote from the subject, it canreceive the data over a network, such as a telephone network or theInternet. To this end, a local computer can be used to transmit the datato the remote computer. This configuration allows performing theanalysis while the subject is at a different location (e.g., at home),and also allows performing simultaneous analyses for multiple subjectsin multiple different locations.

The computational operations of the method can also be executed by acloud computing resource of a cloud computing facility. The cloudcomputing resource can include a computing server and optionally also astorage server, and can be operated by a cloud computing client as knownin the art.

The method according to some embodiments may be used to “rule in” abacterial infection. Alternatively, the method may be used to rule out anon-bacterial infection. The method according to some embodiments can beused to “rule out” a bacterial infection and “rule in” a non-bacterialdisease.

The method according to some embodiments may be used to “rule in” aviral infection. Alternatively, the method may be used to rule out anon-viral infection.

The method according to some embodiments can be used to “rule out” aviral infection and “rule in” a non-viral disease.

The method according to some embodiments may be used to “rule in” aninfectious disease. Alternatively, the method may be used to rule out anon-infectious disease. The method according to some embodiments can beused to “rule out” an infectious disease and “rule in” a non-infectiousdisease.

The biological data analyzed by the method contain expression values ofa plurality of polypeptides in the blood of a subject. In someembodiments the biological data comprises expression values of only twopolypeptides, in some embodiments the biological data comprisesexpression values of at least three polypeptides, in some embodimentsbiological data comprises expression values of only three polypeptides,in some embodiments biological data comprises expression values of atleast four polypeptides, in some embodiments biological data comprisesexpression values of only four polypeptides, in some embodimentsbiological data comprises expression values of at least fivepolypeptides, and in some embodiments biological data comprisesexpression values of only five polypeptides.

The present Inventors contemplate many types of polypeptides.Representative examples include, without limitation, CRP, IP-10, TRAIL,IL1ra, PCT and SAA. In some embodiments the plurality of polypeptidescomprises at least CRP and TRAIL, and in some embodiments the pluralityof polypeptides comprises at least CRP, TRAIL and IP-10.

In some embodiments of the present invention, the biological data isprovided in the form of a subject-specific dataset, as further detailedherein.

According to a particular embodiment, the levels of secreted (i.e.soluble) polypeptides (e.g., TRAIL, CRP and IP-10) are analyzed by themethod.

The term “subject” as used herein is preferably a human. A subject canbe male or female. The subject may be a newborn, baby, infant or adult.A subject can be one who has been previously diagnosed or identified ashaving an infection, and optionally has already undergone, or isundergoing, a therapeutic intervention for the infection. Alternatively,a subject can also be one who has not been previously diagnosed ashaving an infection. For example, a subject can be one who exhibits oneor more risk factors for having an infection. A subject may also have aninfection but show no symptoms of infection.

The subject whose disease is being diagnosed according to someembodiments of the present invention is referred to below as the “testsubject”. The present Inventors have collected knowledge regarding theexpression pattern of polypeptides, of a plurality of subjects whosedisease has already been diagnosed, and have devised the analysistechnique of the present embodiments based on the collected knowledge.This plurality of subjects is referred to below as “pre-diagnosedsubjects” or “other subjects”.

As used herein, the phrase “bacterial infection” refers to a conditionin which a subject is infected with a bacterium. The infection may besymptomatic or asymptomatic. In the context of this invention, thebacterial infection may also comprise a viral component (i.e. be a mixedinfection being the result of both a bacteria and a virus).

The bacterial infection may be acute or chronic.

An acute infection is characterized by rapid onset of disease, arelatively brief period of symptoms, and resolution within days. Achronic infection is an infection that develops slowly and lasts a longtime. One difference between acute and chronic infection is that duringacute infection the immune system often produces IgM+ antibodies againstthe infectious agent, whereas the chronic phase of the infection isusually characteristic of IgM−/IgG+ antibodies. In addition, acuteinfections cause immune mediated necrotic processes while chronicinfections often cause inflammatory mediated fibrotic processes andscaring. Thus, acute and chronic infections may elicit differentunderlying immunological mechanisms.

The bacterial infection may be the result of gram-positive,gram-negative bacteria or atypical bacteria.

The term “Gram-positive bacteria” as used herein refers to bacteriacharacterized by having as part of their cell wall structurepeptidoglycan as well as polysaccharides and/or teichoic acids and arecharacterized by their blue-violet color reaction in the Gram-stainingprocedure. Representative Gram-positive bacteria include: Actinomycesspp., Bacillus anthracis, Bifidobacterium spp., Clostridium botulinum,Clostridium perfringens, Clostridium spp., Clostridium tetani,Corynebacterium diphtheriae, Corynebacterium jeikeium, Enterococcusfaecalis, Enterococcus faecium, Erysipelothrix rhusiopathiae,Eubacterium spp., Gardnerella vaginalis, Gemella morbillorum,Leuconostoc spp., Mycobacterium abcessus, Mycobacterium avium complex,Mycobacterium chelonae, Mycobacterium fortuitum, Mycobacteriumhaemophilium, Mycobacterium kansasii, Mycobacterium leprae,Mycobacterium marinum, Mycobacterium scrofulaceum, Mycobacteriumsmegmatis, Mycobacterium terrae, Mycobacterium tuberculosis,Mycobacterium ulcerans, Nocardia spp., Peptococcus niger,Peptostreptococcus spp., Proprionibacterium spp., Staphylococcus aureus,Staphylococcus auricularis, Staphylococcus capitis, Staphylococcuscohnii, Staphylococcus epidermidis, Staphylococcus haemolyticus,Staphylococcus hominis, Staphylococcus lugdanensis, Staphylococcussaccharolyticus, Staphylococcus saprophyticus, Staphylococcusschleiferi, Staphylococcus similans, Staphylococcus warneri,Staphylococcus xylosus, Streptococcus agalactiae (group Bstreptococcus), Streptococcus anginosus, Streptococcus bovis,Streptococcus canis, Streptococcus equi, Streptococcus milleri,Streptococcus mitior, Streptococcus mutans, Streptococcus pneumoniae,Streptococcus pyogenes (group A streptococcus), Streptococcussalivarius, Streptococcus sanguis.

The term “Gram-negative bacteria” as used herein refer to bacteriacharacterized by the presence of a double membrane surrounding eachbacterial cell.

Representative Gram-negative bacteria include Acinetobactercalcoaceticus, Actinobacillus actinomycetemcomitans, Aeromonashydrophila, Alcaligenes xylosoxidans, Bacteroides, Bacteroides fragilis,Bartonella bacilliformis, Bordetella spp., Borrelia burgdorferi,Branhamella catarrhalis, Brucella spp., Campylobacter spp., Chalmydiapneumoniae, Chlamydia psittaci, Chlamydia trachomatis, Chromobacteriumviolaceum, Citrobacter spp., Eikenella corrodens, Enterobacteraerogenes, Escherichia coli, Flavobacterium meningosepticum,Fusobacterium spp., Haemophilus influenzae, Haemophilus spp.,Helicobacter pylori, Klebsiella spp., Legionella spp., Leptospira spp.,Moraxella catarrhalis, Morganella morganii, Mycoplasma pneumoniae,Neisseria gonorrhoeae, Neisseria meningitidis, Pasteurella multocida,Plesiomonas shigelloides, Prevotella spp., Proteus spp., Providenciarettgeri, Pseudomonas aeruginosa, Pseudomonas spp., Rickettsiaprowazekii, Rickettsia rickettsii, Rochalimaea spp., Salmonella spp.,Salmonella typhi, Serratia marcescens, Shigella spp., Treponemacarateum, Treponema pallidum, Treponema pallidum endemicum, Treponemapertenue, Veillonella spp., Vibrio cholerae, Vibrio vulnificus, Yersiniaenterocolitica and Yersinia pestis.

The term “Atypical bacteria” refers to bacteria that do not fall intoone of the classical “Gram” groups. Typically they are intracellularbacterial pathogens. They include, without limitations, Mycoplasmasspp., Legionella spp. Rickettsiae spp., and Chlamydiae spp.

The term “non-bacterial disease” as used herein, refers to any diseaseor condition that is not caused by infectious bacteria.

Referring to FIG. 31 , the method begins at 310 and continues to 311 atwhich a first distance d between a segment S_(ROI) of a first curvedobject S and a non-curved object π is calculated. Generally, the firstcurved object S is a manifold in n dimensions, where n is a positiveinteger, and the non-curved object π is a hyperplane in an n+1dimensional space.

The concept of n-dimensional manifolds and hyperplanes in n+1 dimensionsare well known to those skilled in the art of geometry. For example,when n=1 the first curved object is a curved line, and the non-curvedobject π is a hyperplane in 2 dimensions, namely a straight linedefining an axis. When n=2, the first curved object is a curved surface,and the non-curved object π is a hyperplane in 3 dimensions, namely aflat plane, referred to below as “a plane”.

The hyperplane 7C is defined by n directions. For example, when thenon-curved object is an axis, it is defined by a single direction, andwhen the non-curved object is a plane it is defined by two directions,referred to as a first direction and a second direction.

The distance between the manifold S and hyperplane π is calculated at apoint P over the hyperplane. P is defined by n coordinates. For example,when the hyperplane is an axis, P is defined by a single coordinate δ₁,along the single direction, and when the hyperplane is a plane, P isdefine by a pair of coordinates denoted (δ₀, δ₁), where δ₀ is referredto as “a first coordinate” and is defined along the first direction, andδ₁ is referred to as “a second coordinate” and is defined along thesecond direction. Unless explicitly stated otherwise, a reference tocoordinate δ₀ describes an optional embodiment which is contemplatedwhen S is a surface and π is a plane.

The directions are denoted using the same Greek letters as therespective coordinates, except that the directions are denoted byunderlined Greek letters to indicate that these are vectors. Thus, thefirst direction is denoted δ₀, and the second direction is denoted δ₁.

FIG. 32A illustrates the hyperplane π for the case of n=2. In theseembodiments, π is a plane defined by directions δ₀ and δ₁. Also shown isa point P at (δ₀, δ₁). Directions δ₀ and δ₁, are shown orthogonal toeach other, but this need not necessarily be the case, since the anglebetween δ₀ and δ₁ can be different from 90°. Within the plane π, thereis a planar region-of-interest δ_(ROI) spanning from a minimal firstcoordinate δ_(0,MIN) to a maximal first coordinate δ_(0,MAX) alongdirection δ₀, and from a minimal second coordinate δ_(1,MIN) to amaximal second coordinate δ_(1,MAX) along direction δ₁. The point P iswithin the region-of-interest π_(ROI). When n=1 (not shown), π is anaxis and the region-of-interest π_(ROI) is a linear segment of πspanning from δ_(1,MIN) to δ_(1,MAX) along direction δ₁.

The calculation of the first distance d is illustrated in FIG. 32B whichillustrates the hyperplane π and manifold S. The distance d is measuredfrom S to the point P, perpendicularly to π. It is to be understood thatwhile each of objects π and S is illustrated as a one dimensional line,this need not necessarily be the case, since S and π are generallyn-dimensional mathematical objects. For example, when S is a surface andπ is a plane both π and S are two dimensional mathematical objects. Thesegment S_(ROI) of S is above a region-of-interest π_(ROI). For example,when π is a plane π_(ROI) is a planar region-of-interest, and when π isan axis, π_(ROI) is a linear segment along the axis. Thus, π_(ROI) isthe projection of S_(ROI) on π. For n=2, S_(ROI) is preferably anon-planar segment of (the surface) S, and for n=1, S_(ROI) ispreferably a curved segment of (the curve) S.

Each of the n coordinates is defined by a combination of expressionvalues of the polypeptides. For example, for n=1, the coordinate δ₁ isdefined by a combination of expression values of the polypeptides, andfor n=2 each of the coordinates δ₀ and δ₁ is defined by a differentcombination of expression values of the polypeptides.

For example, δ₁ and optionally also δ₀ are combinations of thepolypeptides, according to the following equation:

δ₀ =a ₀ +a ₁ D ₁ +a ₂ D ₂+ . . . +ϕ₀

δ₁ =b ₀ +b ₁ D ₁ +b ₂ D ₂+ . . . +ϕ₁,

where a₀, a₁, . . . and b₀, b₁, . . . are constant and predeterminedcoefficients, and each of the variables D₁, D₂, . . . is an expressionlevels of one of the polypeptides, and ϕ₀ and ϕ₁ are functions that arenonlinear with respect to at least one of the expression levels.

Each of the functions ϕ₀ and ϕ₁ is optional and may, independently, beset to zero (or, equivalently, not included in the calculation of therespective coordinate). When ϕ₀=0 the coordinate δ₀ is a combination ofthe polypeptides, and when ϕ₁=0 the coordinate δ₁ is a combination ofthe polypeptides.

The nonlinear functions ϕ₀ and ϕ₁ can optionally and preferably beexpressed as a sub of powers of expression levels, for example,according to the following equations:

ϕ₀=Σ_(i) q _(i) X _(i) ^(γi)

ϕ₁=Σ_(i) r _(i) X _(i) ^(λi),

where i is a summation index, q_(i) and r_(i) are sets of coefficients,X_(i)∈{D₁, D₂, . . . }, and each of γi and λi is a numerical exponent.Note that the number of terms in each of the nonlinear functions ϕ₀ andϕ₁ does not necessarily equals the number of the polypeptides, and thattwo or more terms in each sum may correspond to the same polypeptide,albeit with a different numerical exponent.

Representative examples of coefficients suitable for the presentembodiments are provided in the Examples section that follows (seeTables 3, 13-17, 29 and 31-36).

When ϕ₀=0, ϕ₁=0 and the polypeptides include TRAIL, δ₀ is optionally andpreferably an increasing function of an expression value of TRAIL, andδ₁ is a decreasing function of TRAIL. When ϕ₀=0, ϕ₁=0 and thepolypeptides include CRP, δ₁ and optionally also δ₀ are optionally andpreferably increasing functions of an expression value of CRP. When thepolypeptides include IP-10, δ₁ and optionally also δ₀ are optionally andpreferably are increasing functions of an expression value of IP-10.

In embodiments in which ϕ₀=0, ϕ₁=0 and the polypeptides include TRAIL,CRP and IP-10, each δ₀ and δ₁ can be a linear combination of TRAIL, CRPand IP-10, according to the following equation:

δ₀ =a ₀ +a ₁ C+a ₂ I+a ₃ T

δ₁ =b ₀ +b ₁ C+b ₂ I+b ₃ T,

where C, I and T are, respectively, the expression levels of CRP, IP-10and TRAIL.

Preferably, both a₁ and b₁ are positive. Preferably both a₂ and b₂ arepositive.

Preferably, a₃ is positive, and b₃ is negative. Representative examplesof coefficients suitable for the embodiments in which the combination islinear combination and the polypeptides are CRP, IP-10 and TRAIL areprovided in the Examples section that follows (see Tables 3, 13-17 and33).

In embodiments in which ϕ₀≠0, ϕ₁≠0 and the polypeptides include TRAIL,CRP and IP-10, each δ₀ and δ₁ can be a combination of TRAIL, CRP andIP-10, according to the following equations:

δ₀ =a ₀ +a ₁ C+a ₂ I+a ₃ T+ϕ ₀

δ₁ =b ₀ +b ₁ C+b ₂ I+b ₃ T+ϕ ₁,

where each of ϕ₀ and ϕ₁ is a nonlinear function of at least one or atleast two of C, I and T. As a representative example, ϕ₀ and ϕ₁ can beexpressed as:

ϕ₀ =q ₁ C ^(γ1) +q ₂ C ^(γ2) +q ₃ T ^(γ3)

ϕ₁ =r ₁ C ^(γ1) +r ₂ C ^(γ2) +r ₃ T ^(γ3).

Representative examples of coefficients suitable for the embodiments inwhich the polypeptides are CRP, IP-10 and TRAIL and the nonlinearfunctions are not taken to be zero are provided in the Examples sectionthat follows (see Table 36).

The boundaries δ0,MIN, δ_(0,MAX), δ_(1,MIN) and δ_(1,MAX) of π_(ROI)preferably correspond to the physiologically possible ranges of theexpression values of the polypeptides.

When measured using the protocols described in Example 8, morepreferably Example 9, below, the physiologically possible ranges aretypically from 0 to about 400 ug/ml (CRP), from 0 to about 3000 pg/ml(IP-10), and from 0 to about 700 pg/ml (TRAIL). Some subjects mayexhibit concentrations that lie outside these ranges. In variousexemplary embodiments of the invention, when the expression values ofTRAIL, CRP and IP-10 are measured according to the protocol described inExample 8, more preferably Example 9, below, the values of thecoefficients a₀, . . . , a₃ and b₀, . . . , b₃ are taken from Table 3,below, and the boundaries of π_(ROI) are: δ_(0,MIN)=−1.3 δ_(0,MAX)=45δ_(1,MIN)=−14.3 and δ_(1,MAX)=49.6.

When the expression values of TRAIL, CRP and IP-10 are measured by aprotocol which is different from the protocol described in Example 8,more preferably Example 9, below, the values of the coefficients a₀, . .. , a₃ and b₀, . . . , b₃ are different from the values in Table 3below, and therefore the boundaries of π_(ROI) are also different fromthe above values. In such cases, the values of the coefficients andboundaries are correlative to the aforementioned values wherein thecorrelation for each coefficient and boundary is derived from thecorrelation between the expression value of the respective protein asmeasured according to the protocol described in Example 8, morepreferably Example 9, and the expression value of the respective proteinas actually measured.

At least a major part of the segment S_(ROI) of curved object S isbetween two curved objects referred to below as a lower bound curvedobject S_(LB) and an upper bound curved object S_(UB).

As used herein “major part of the segment S_(ROI)” refers to a part of asmoothed version S_(ROI) whose length (when n=1), surface area (whenn=2) or volume (when n≥3) is 60% or 70% or 80% or 90% or 95% or 99% of asmoothed version of the length, surface area or volume of S_(ROI),respectively.

As used herein, “a smooth version of the segment S_(ROI)” refers to thesegment S_(ROI), excluding regions of S_(ROI) at the vicinity of pointsat which the Gaussian curvature is above a curvature threshold, which isX times the median curvature of S_(ROI), where X is 1.5 or 2 or 4 or 8.

The following procedure can be employed for the purpose of determiningwhether the major part of the segment S_(ROI) is between S_(LB) andS_(UB). Firstly, a smoothed version of the segment S_(ROI) is obtained.Secondly, the length (when n=1), surface area (when n=2) or volume (whenn≥3) A₁ of the smoothed version of the segment S_(ROI) is calculated.Thirdly, the length (when n=1) surface area (when n=2) or volume (whenn≥3) A₂ of the part of the smoothed version of the segment S_(ROI) thatis between S_(LB) and S_(UB) is calculated. Fourthly, the percentage ofA₂ relative to A₁ is calculated.

FIGS. 33A-33D illustrates a procedure for obtaining the smooth versionof S_(ROI).

For clarity of presentation, S_(ROI) is illustrated as a one dimensionalsegment, but the skilled person would understand that S_(ROI) isgenerally an n-dimensional mathematical object. The Gaussian curvatureis calculated for a sufficient number of sampled points on S_(ROI). Forexample, when the manifold is represented as point cloud, the Gaussiancurvature can be calculated for the points in the point cloud. Themedian of the Gaussian curvature is then obtained, and the curvaturethreshold is calculated by multiplying the obtained median by the factorX. FIG. 33A illustrates S_(ROI) before the smoothing operation. Markedis a region 320 having one or more points 322 at which the Gaussiancurvature is above the curvature threshold. The point or points at whichwith the Gaussian curvature is maximal within region 320 is removed andregion 320 is smoothly interpolated, e.g., via polynomial interpolation,(FIG. 33B). The removal and interpolation is repeated iteratively (FIG.33C) until the segment S_(ROI) does not contain regions at which theGaussian curvature is above the curvature threshold (FIG. 33D).

When n=1 (namely when S is a curved line), S_(LB) is a lower boundcurved line, and S_(UB) an upper bound curved line. In theseembodiments, S_(LB) and S_(UB) can be written in the form:

S _(LB) =f(δ₁)−ε₀,

S _(UB) =f(δ₁)+ε₁

where f(δ₁) is a probabilistic classification function of the coordinateδ₁ (along the direction δ₁) which represents the likelihood that thetest subject has a bacterial infection. In some embodiments of theinvention f(δ₁)=1/(1+exp(δ₁)). Both S_(LB) and S_(UB) are positive forany value of δ₁ within π_(ROI). Also contemplated, are embodiments inwhich f(δ₁) is a probabilistic classification function which representsthe likelihood that the test subject has a viral infection. Furthercontemplated, are embodiments in which f(δ₁) is a probabilisticclassification function which represents the likelihood that the testsubject has an infection.

When n=2 (namely when S is a curved surface), S_(LB) is a lower boundcurved surface, and S_(UB) an upper bound curved surface. In theseembodiments, S_(LB) and S_(UB) can be written in the form:

S _(LB) =f(δ₀,δ₁)−ε₀,

S _(UB) =f(δ₀,δ₁)+ε₁

where f(δ₀,δ₁) is a probabilistic classification function of the firstand second coordinates (along the first and second directions) whichrepresents the likelihood that the test subject has a bacterialinfection. In some embodiments of the inventionf(δ₀,δ₁)=exp(δ₁)/(1+exp(δ₀)+exp(δ₁)). Both S_(LB) and S_(UB) arepositive for any value of δ₀ and δ₁ within π_(ROI).

In any of the above embodiments each of the parameters ε₀ and ε₁ is lessthan 0.5 or less than 0.4 or less than 0.3 or less than 0.2 or less than0.1 or less than 0.05.

Referring again to FIG. 31 , the method proceeds to 312 at which thecalculated distance d is correlated to the presence of, absence of, orlikelihood that the subject has, a disease or condition corresponding tothe type of the probabilistic function f. For example, when theprobabilistic function f represents the likelihood that the test subjecthas a bacterial infection, the calculated distance d is correlated tothe presence of, absence of, or likelihood that the subject has, abacterial infection.

In various exemplary embodiments of the invention the correlationincludes determining that the distance d is the likelihood that thesubject has a bacterial infection. The likelihood is optionally andpreferably compared to a predetermined threshold ω_(B), wherein themethod can determine that it is likely that the subject has a bacterialinfection when the likelihood is above ω_(B), and that it is unlikelythat the subject has a bacterial infection otherwise. Typical values forω_(B) include, without limitation, about 0.2, about 0.3, about 0.4,about 0.5, about 0.6 and about 0.7. Other likelihood thresholds are alsocontemplated.

In some embodiments of the present invention, when the method determinesthat it is likely that the subject has a bacterial infection, thesubject is treated (316) for the bacterial infection, as furtherdetailed herein.

The present inventors found a probabilistic classification functiong(δ₀,δ₁) which represents the likelihood that the test subject has aviral infection. In various exemplary embodiments of the inventiong(δ₀,δ₁) equals exp(δ₀)/(1+exp(δ₀)+exp(δ₁)).

The function g can, according to some embodiments of the presentinvention, be utilized also for estimating the presence of, absence of,or likelihood that the subject has, a viral infection. Thus, in someembodiments, the method proceeds to 313 at which a second distancebetween a segment of a second curved surface and the plane π iscalculated, and 314 at which the second distance is correlated to thepresence of, absence of, or likelihood that the subject has, a viralinfection. The procedure and definitions corresponding to 313 and 314are similar to the procedure and definitions corresponding to 311 and312 described above, mutatis mutandis. Thus, for example, a major partof the segment of the second surface is between a second lower boundsurface g(δ₀,δ₁)−ε₂ and a second upper bound surface g(δ₀,δ₁)+ε₃,wherein each of ε₂ and ε₃ is less than 0.5 or less than 0.4 or less than0.3 or less than 0.2 or less than less than 0.1 or less than 0.05.

In some embodiments of the present invention, when the method determinesthat it is likely that the subject has a viral infection, the subject istreated (316) for the viral infection, as further detailed herein.

In various exemplary embodiments of the invention the correlationincludes determining that the second distance is the likelihood that thesubject has a viral infection. The likelihood is optionally andpreferably compared to a predetermined threshold ω_(V), wherein themethod can determine that it is likely that the subject has a viralinfection when the likelihood is above ω_(V), that it is unlikely thatthe subject has a viral infection otherwise. Typical values for ω_(V)include, without limitation, about 0.5, about 0.6 about 0.7 and about0.8. Other likelihood thresholds are also contemplated.

In embodiments in which operations 313 and 314 are executed, operations311 and 312 can be either executed or not executed. For example, thepresent embodiments contemplate a procedure in which operations 311 and312 are not executed, and the method determines the likelihood that thesubject has a viral infection, without calculating the first distanceand without correlating the first distance to the presence of, absenceof, or likelihood that the subject has, a bacterial infection.

Alternatively, all operations 311-314 can be executed, wherein 311 and312 are executed irrespectively of the outcome of 314, and 313 and 314are executed irrespectively of the outcome of 312. In these embodiments,the method optionally and preferably determines both the likelihood thatthe subject has a bacterial infection, and the likelihood that thesubject has a viral infection. Each of these likelihoods can be comparedto the respective predetermined threshold (ω_(B) or ω_(V)). When each ofthe likelihoods is below the respective threshold, the method candetermine that the patient is likely to have a non-bacterial andnon-viral infectious disease. For example, the method can determine thatit is likely that the subject has a non-infectious disease, a fungaldisease or a parasitic disease.

Still alternatively, whether or not some operations are executed isdependent on the outcome of one or more other operations. For example,the method can execute 311 and 312, so as to determine the likelihoodthat the subject has a bacterial infection. Thereafter, the determinedlikelihood is compared to the threshold WB. The method skips theexecution of 313 and 314 if the determined likelihood is above WB, andexecutes 313 and 314 otherwise. Another example of these embodiments isa procedure in which the method executes 313 and 314, so as to determinethe likelihood that the subject has a viral infection. Thereafter, thedetermined likelihood is compared to the threshold ω_(V). The methodskips the execution of 311 and 312 if the determined likelihood is aboveω_(V), and executes 311 and 312 otherwise.

The method optionally and preferably continues to 315 at which an outputof the likelihood(s) is generated. The output can be presented as text,and/or graphically and/or using a color index. The output can optionallyinclude the results of the comparison to the threshold WB. FIGS. 29A-29Fand 38A-38E illustrate exemplary outputs suitable for distinguishingbetween bacterial and non-bacterial infection according to an embodimentof the present invention.

The method ends at 317.

FIGS. 38A-38E are screenshots of a graphical user interface (GUI)suitable for receiving user input in a computer-implemented method foranalyzing biological data according to some embodiments of the presentinvention.

The GUI comprises a calculation activation control 390, that may be inthe form of a button control. The GUI may also comprise a plurality ofexpression value input fields 380, wherein each expression value inputfield is configured for receiving from a user an expression value of apolypeptide in the blood of a subject. The user feeds into the inputfields the expression values of the polypeptides. Alternatively, theexpression values are can be received by establishing a communicationbetween the computer and an external machine (not shown) that measuresthe expression values. In these embodiments, it is not necessary for theuser to manually feed the expression values into the input fields. Insome embodiments, the GUI comprises a communication control 392, e.g.,in the form of a button control, wherein the communication with theexternal machine is in response to an activation of the communicationcontrol by the user.

Responsively to an activation of control 390 by the user, the computercalculates a score based on the expression values as receivedautomatically or via fields 380. The core can be the likelihood that thesubject has a bacterial infection and/or a viral infection. The scorecan be calculated for example, by calculating a distance between acurved surface and a plane defined by the two directions as furtherdetailed hereinabove.

A graphical scale 382 can be generated on the GUI. The graphical scalecan include a first end, identified as corresponding to a viralinfection, and a second end, identified as corresponding to a bacterialinfection.

Once the score is calculated, a mark 394 can optionally and preferablybe made on the graphical 382 at a location corresponding to thecalculated likelihood. FIG. 38A shows the GUI before the values havebeen fed into the input fields, FIG. 38B shows mark 394 on scale 382 ata location that corresponds to a likelihood of 96% that the infection isbacterial, and FIG. 38C shows mark 394 on scale 382 at a location thatcorresponds to a likelihood of 1% that the infection is bacterial (or,equivalently, likelihood of 99% that the infection is viral).Optionally, the GUI also displays the calculated score numerically.

The GUI optionally and preferably includes one or more additionalcontrols 386, 388 that may be in the form of button controls. Forexample, control 388 can instruct the computer to clear the input fields380 when the user activates the control 388. This allows the user tofeed values that correspond to a different sample. In some embodiments,the GUI also generates an output 384 that summarizes the results of theprevious samples. Control 386 can instruct the computer to clear theinput fields 380 as well as the output 384 when the user activates thecontrol 386. This allows the user to begin a new run (optionally withmultiple samples) without logging out of the GUI.

A representative example of a protocol suitable for the presentembodiments is as follows.

The GUI presents an authenticated user with a dialog that allows theuser to feed in quality control (QC) values of an experiment. The QC isvalidated, and the GUI in FIG. 38A is generated. The user feeds in theexpression values in fields 380 and activate control 390 to receive theresult (e.g., FIGS. 38B and 38C). To feed in expression values ofanother blood sample the user activates control 388. The result of eachsample is added to output 384 which can be, for example, in the form ofa table. To enter a new experiment without closing the software orlogging out the user activates control 386 to clear output 384 and enternew QC values. Preferably, all the operations are logged in one or morelog files.

In some embodiments of the present invention GUI also includes a reportscreen (FIGS. 38D and 38E) that displays the results of previousexperiments, for example, in response to a date based request.

It will be appreciated that the polypeptide names presented herein aregiven by way of example. Many alternative names, aliases, modifications,isoforms and variations will be apparent to those skilled in the art.Accordingly, it is intended to embrace all the alternative proteinnames, aliases, modifications isoforms and variations.

Gene products, are identified based on the official letter abbreviationor gene symbol assigned by the international Human Genome OrganizationNaming Committee (HGNC) and listed at the date of this filing at the USNational Center for Biotechnology Information (NCBI) web site also knownas Entrez Gene.

TRAIL: The protein, TNF Related Apoptosis Inducing Ligand (TRAIL),encoded by this gene is a cytokine that belongs to the tumor necrosisfactor (TNF) ligand family. Additional names of the gene include withoutlimitations APO2L, TNF-related apoptosis-inducing ligand, TNFSF10 andCD253. TRAIL exists in a membrane bound form and a soluble form, both ofwhich can induce apoptosis in different cells, such as transformed tumorcells. This protein binds to several members of the TNF receptorsuperfamily such as TNFRSF10A/TRAILR1, NFRSF10B/TRAILR2,NFRSF10C/TRAILR3, TNFRSF10D/TRAILR4, and possibly also to NFRSF11B/OPG.The activity of this protein may be modulated by binding to the decoyreceptors such as NFRSF10C/TRAILR3, TNFRSF10D/TRAILR4, and NFRSF11B/OPGthat cannot induce apoptosis. The binding of this protein to itsreceptors has been shown to trigger the activation of MAPK8/JNK, caspase8, and caspase 3. Alternatively spliced transcript variants encodingdifferent isoforms have been found for this gene. TRAIL can beproteolytically cleaved from the cell surface to produce a soluble formthat has a homotrimeric structure.

According to a particular embodiment, the level of the soluble (i.e.secreted) form of TRAIL is measured.

According to another embodiment, the membrane form of TRAIL is measured.

According to still another embodiment, both the membrane form of TRAILand the secreted form of TRAIL are measured.

According to another aspect of the present invention there is provided amethod of determining an infection type in a subject comprisingmeasuring the concentration of soluble TRAIL and insoluble TRAIL,wherein the concentration is indicative of the infection type.

In one embodiment, when the concentration of the soluble TRAIL is higherthan a pre-determined threshold value, a bacterial infection is ruledout for the subject.

In another embodiment, when the concentration of the soluble TRAIL ishigher than a pre-determined threshold value, a viral infection is ruledin for the subject.

Exemplary protein sequences for soluble TRAIL are set forth in SEQ IDNO: 37 and SEQ ID NO: 38.

An exemplary mRNA sequence of membrane human TRAIL is set forth in SEQID NO: 1.

An exemplary amino acid sequences of membrane human TRAIL is set forthin SEQ ID NOs: 4.

Other exemplary cDNA and amino acid sequences for TRAIL are set forth inSEQ ID NOs: 2, 3 and 5-8.

IP10: This gene encodes a chemokine of the CXC subfamily and ligand forthe receptor CXCR3. Binding of this protein to CXCR3 results inpleiotropic effects, including stimulation of monocytes, natural killerand T-cell migration, and modulation of adhesion molecule expression.Additional names of the gene include without limitations: IP-10, CXCL10,Gamma-IP10, INP10 and chemokine (C—X—C motif) ligand 10.

Exemplary cDNA sequence of human IP10 is set forth in SEQ ID NOs: 9-12.An exemplary amino acid sequence of human IP10 is set forth in SEQ IDNO: 13.

CRP: C-reactive protein; additional aliases of CRP include withoutlimitation RP11-419N10.4 and PTX1. The protein encoded by this genebelongs to the pentaxin family. It is involved in several host defenserelated functions based on its ability to recognize foreign pathogensand damaged cells of the host and to initiate their elimination byinteracting with humoral and cellular effector systems in the blood.Consequently, the level of this protein in plasma increases greatlyduring acute phase response to tissue injury, infection, or otherinflammatory stimuli. CRP displays several functions associated withhost defense: it promotes agglutination, bacterial capsular swelling,phagocytosis and complement fixation through its calcium-dependentbinding to phosphorylcholine.

Exemplary cDNA sequence of human CRP is set forth in SEQ ID NOs: 14-16.

An exemplary amino acid sequence of human CRP is set forth in SEQ ID NO:17.

IL1RA: The protein encoded by this gene is a cytokine receptor thatbelongs to the interleukin 1 receptor family. This protein is a receptorfor interleukin alpha (IL1A), interleukin beta (IL1B), and interleukin 1receptor, type I (IL1R1/IL1RA). It is an important mediator involved inmany cytokine induced immune and inflammatory responses. Additionalnames of the gene include without limitations: CD121A, IL-1RT1, p80,CD121a antigen, CD121A, IL1R and IL1ra.

Exemplary cDNA sequences of human IL1RA are set forth in SEQ ID NOs: 18,19 and 20.

Exemplary amino acid sequences of human IL1RA are set forth in SEQ IDNOs:21-24.

PCT: Procalcitonin (PCT) is a peptide precursor of the hormonecalcitonin, the latter being involved with calcium homeostasis.Procalcitonin (“pCT”) is a protein consisting of 116 amino acids andhaving a molecular weight of about 13,000 dalton. It is the prohormoneof calcitonin which under normal metabolic conditions is produced andsecreted by the C cells of the thyroid. pCT and calcitonin synthesis isinitiated by translation of preprocalcitonin (“pre-pCT”), a precursorpeptide comprising 141 amino acids. The amino acid sequence of humanpre-pCT was described by Moullec et al. in FEBS Letters, 167:93-97 in1984. pCT is formed after cleavage of the signal peptide (first 25 aminoacids of pre-pCT).

Exemplary cDNA sequences of human PCT are set forth in SEQ ID NOs:31-32.

Exemplary amino acid sequences of human PCT are set forth in SEQ IDNOs:33-36.

SAA: encodes a member of the serum amyloid A family of apolipoproteins.The encoded protein is a major acute phase protein that is highlyexpressed in response to inflammation and tissue injury. This proteinalso plays an important role in HDL metabolism and cholesterolhomeostasis. High levels of this protein are associated with chronicinflammatory diseases including atherosclerosis, rheumatoid arthritis,Alzheimer's disease and Crohn's disease. This protein may also be apotential biomarker for certain tumors. Alternate splicing results inmultiple transcript variants that encode the same protein.

Exemplary cDNA sequences of human SAA are set forth in SEQ ID NOs:25-27.

Exemplary amino acid sequences of human SAA are set forth in SEQ IDNO:28-30.

It will be appreciated that since patient to patient DNA variations maygive rise to SNPs which can cause differences in the amino acid sequenceof the proteins, the present inventors also contemplate proteins havingamino acid sequences at least 90%, 95% or 99% homologous to thesequences provided herein above.

Measuring the polypeptide (for example, TRAIL, IP-10 and CRP) levels istypically affected at the protein level as further described hereinbelow.

Methods of Detecting Expression and/or Activity of Proteins

Expression and/or activity level of proteins expressed in the cells ofthe cultures of some embodiments of the invention can be determinedusing methods known in the arts and typically involve the use ofantibodies. Such methods may be referred to as immunoassays.Immunoassays may be run in multiple steps with reagents being added andwashed away or separated at different points in the assay. Multi-stepassays are often called separation immunoassays or heterogeneousimmunoassays. Some immunoassays can be carried out simply by mixing thereagents and sample and making a physical measurement. Such assays arecalled homogenous immunoassays or less frequently non-separationimmunoassays. The use of a calibrator is often employed in immunoassays.Calibrators are solutions that are known to contain the analyte inquestion, and the concentration of that analyte is generally known.Comparison of an assay's response to a real sample against the assay'sresponse produced by the calibrators makes it possible to interpret thesignal strength in terms of the presence or concentration of analyte inthe sample.

The antibody may be monoclonal, polyclonal, chimeric, or a fragment ofthe foregoing, and the step of detecting the reaction product may becarried out with any suitable immunoassay.

Suitable sources for antibodies for the detection of the polypeptidesinclude commercially available sources such as, for example, Abazyme,Abnova, AssayPro, Affinity Biologicals, AntibodyShop, Aviva bioscience,Biogenesis, Biosense Laboratories, Calbiochem, Cell Sciences, ChemiconInternational, Chemokine, Clontech, Cytolab, DAKO, DiagnosticBioSystems, eBioscience, Endocrine Technologies, Enzo Biochem,Eurogentec, Fusion Antibodies, Genesis Biotech, GloboZymes, HaematologicTechnologies, Immunodetect, Immunodiagnostik, Immunometrics, Immunostar,Immunovision, Biogenex, Invitrogen, Jackson ImmunoResearch Laboratory,KMI Diagnostics, Koma Biotech, LabFrontier Life Science Institute, LeeLaboratories, Lifescreen, Maine Biotechnology Services, Mediclone,MicroPharm Ltd., ModiQuest, Molecular Innovations, Molecular Probes,Neoclone, Neuromics, New England Biolabs, Novocastra, Novus Biologicals,Oncogene Research Products, Orbigen, Oxford Biotechnology, Panvera,PerkinElmer Life Sciences, Pharmingen, Phoenix Pharmaceuticals, PierceChemical Company, Polymun Scientific, Polysiences, Inc., PromegaCorporation, Proteogenix, Protos Immunoresearch, QED Biosciences, Inc.,R&D Systems, Repligen, Research Diagnostics, Roboscreen, Santa CruzBiotechnology, Seikagaku America, Serological Corporation, Serotec,SigmaAldrich, StemCell Technologies, Synaptic Systems GmbH, Technopharm,Terra Nova Biotechnology, TiterMax, Trillium Diagnostics, UpstateBiotechnology, US Biological, Vector Laboratories, Wako Pure ChemicalIndustries, and Zeptometrix. However, the skilled artisan can routinelymake antibodies, against any of the polypeptides described herein.

Polyclonal antibodies for measuring polypeptides include withoutlimitation antibodies that were produced from sera by activeimmunization of one or more of the following: Rabbit, Goat, Sheep,Chicken, Duck, Guinea Pig, Mouse, Donkey, Camel, Rat and Horse.

Examples of additional detection agents, include without limitation:scFv, dsFv, Fab, sVH, F(ab′)₂, Cyclic peptides, Haptamers, Asingle-domain antibody, Fab fragments, Single-chain variable fragments,Affibody molecules, Affilins, Nanofitins, Anticalins, Avimers, DARPins,Kunitz domains, Fynomers and Monobody.

Enzyme linked immunosorbent assay (ELISA): Performing an ELISA involvesat least one antibody with specificity for a particular antigen. Thesample with an unknown amount of antigen is immobilized on a solidsupport (usually a polystyrene microtiter plate) either non-specifically(via adsorption to the surface) or specifically (via capture by anotherantibody specific to the same antigen, in a “sandwich” ELISA). After theantigen is immobilized, the detection antibody is added, forming acomplex with the antigen. The detection antibody can be covalentlylinked to an enzyme, or can itself be detected by a secondary antibodythat is linked to an enzyme through bioconjugation. Between each step,the plate is typically washed with a mild detergent solution to removeany proteins or antibodies that are aspecifically bound. After the finalwash step, the plate is developed by adding an enzymatic substrate toproduce a visible signal, which indicates the quantity of antigen in thesample.

Enzymes commonly employed in this method include horseradish peroxidaseand alkaline phosphatase. If well calibrated and within the linear rangeof response, the amount of substrate present in the sample isproportional to the amount of color produced. A substrate standard isgenerally employed to improve quantitative accuracy.

Western blot: This method involves separation of a substrate from otherprotein by means of an acrylamide gel followed by transfer of thesubstrate to a membrane (e.g., nylon or PVDF). Presence of the substrateis then detected by antibodies specific to the substrate, which are inturn detected by antibody binding reagents. Antibody binding reagentsmay be, for example, protein A, or other antibodies. Antibody bindingreagents may be radiolabeled or enzyme linked as described hereinabove.Detection may be by autoradiography, colorimetric reaction orchemiluminescence. This method allows both quantitation of an amount ofsubstrate and determination of its identity by a relative position onthe membrane which is indicative of a migration distance in theacrylamide gel during electrophoresis.

Fluorescence activated cell sorting (FACS): This method involvesdetection of a substrate in situ in cells by substrate specificantibodies. The substrate specific antibodies are linked tofluorophores. Detection is by means of a cell sorting machine whichreads the wavelength of light emitted from each cell as it passesthrough a light beam. This method may employ two or more antibodiessimultaneously.

Automated Immunoassay: An automated analyzer applied to an immunoassay(often called “Automated Immunoassay”) is a medical laboratoryinstrument designed to measure different chemicals and othercharacteristics in a number of biological samples quickly, with minimalhuman assistance. These measured properties of blood and other fluidsmay be useful in the diagnosis of disease. Many methods of introducingsamples into the analyzer have been invented. This can involve placingtest tubes of sample into racks, which can be moved along a track, orinserting tubes into circular carousels that rotate to make the sampleavailable. Some analyzers require samples to be transferred to samplecups. However, the effort to protect the health and safety of laboratorystaff has prompted many manufacturers to develop analyzers that featureclosed tube sampling, preventing workers from direct exposure tosamples. Samples can be processed singly, in batches, or continuously.Examples of automated immunoassay machines include, without limitation,ARCHITECT ci4100, ci8200 (2003), ci16200 (2007), ARCHITECT i1000SR,ARCHITECT i2000, i2000SR, i4000SR, AxSYM/AxSYM Plus, 1994 U.S., DS2,AIMS, AtheNA, DSX, ChemWell, UniCel Dxl 860i Synchron Access ClinicalSystem, UniCel DxC 680i Synchron Access Clinical System, Access/Access 2Immunoassay System, UniCel Dxl 600 Access Immunoassay System, UniCel DxC600i Synchron Access Clinical System, UniCel Dxl 800 Access ImmunoassaySystem, UniCel DxC 880i Synchron Access Clinical System, UniCel Dxl 660iSynchron Access Clinical System, SPA PLUS (Specialist Protein Analyzer),VIDAS Immunoassay Analyzer, BioPlex 2200, PhD System EVOLIS PR 3100TSCPhotometer, MAGO 4S/2011 Mago Plus Automated EIA Processor, LIAISONXL/2010 LIAISON, ETI-MAX 3000 Agility, Triturus, HYTEC 288 PLUSDSX,VITROS ECi Immunodiagnostic System, VITROS 3600 Immunodiagnostic System,Phadia Laboratory System 100E, Phadia Laboratory System 250, PhadiaLaboratory System 1000, Phadia Laboratory System 2500, Phadia LaboratorySystem 5000, cobas e 602/2010, cobas e411, cobas e601, MODULAR ANALYTICSE170, Elecsys 2010, Dimension EXL 200/2011, Dimension Xpand PlusIntegrated Chemistry System, Dimension RxL Max/Max Suite IntegratedChemistry System; Dimension RxL Integrated Chemistry System, DimensionEXL with LM Integrated Chemistry System, Stratus CS Acute CareDiagnostic System, IMMULITE 2000 XPi Immunoassay System, ADVIA CentaurCP Immunoassay System, IMMULITE 2000, IMMULITE 1000, Dimension Vista 500Intelligent Lab System, Dimension Vista 1500 Intelligent Lab System,ADVIA Centaur XP, AIA-900, AIA-360, AIA-2000, AIA-600 II, AIA-1800.Measurements of CRP, IP-10 and TRAIL can also be performed on a Luminexmachine.

Lateral Flow Immunoassays (LFIA): This is a technology which allowsrapid measurement of analytes at the point of care (POC) and itsunderlying principles are described below. According to one embodiment,LFIA is used in the context of a hand-held device.

The technology is based on a series of capillary beds, such as pieces ofporous paper or sintered polymer. Each of these elements has thecapacity to transport fluid (e.g., urine) spontaneously. The firstelement (the sample pad) acts as a sponge and holds an excess of samplefluid. Once soaked, the fluid migrates to the second element (conjugatepad) in which the manufacturer has stored the so-called conjugate, adried format of bio-active particles (see below) in a salt-sugar matrixthat contains everything to guarantee an optimized chemical reactionbetween the target molecule (e.g., an antigen) and its chemical partner(e.g., antibody) that has been immobilized on the particle's surface.While the sample fluid dissolves the salt-sugar matrix, it alsodissolves the particles and in one combined transport action the sampleand conjugate mix while flowing through the porous structure. In thisway, the analyte binds to the particles while migrating further throughthe third capillary bed. This material has one or more areas (oftencalled stripes) where a third molecule has been immobilized by themanufacturer. By the time the sample-conjugate mix reaches these strips,analyte has been bound on the particle and the third ‘capture’ moleculebinds the complex.

After a while, when more and more fluid has passed the stripes,particles accumulate and the stripe-area changes color. Typically thereare at least two stripes: one (the control) that captures any particleand thereby shows that reaction conditions and technology worked fine,the second contains a specific capture molecule and only captures thoseparticles onto which an analyte molecule has been immobilized. Afterpassing these reaction zones the fluid enters the final porous material,the wick, that simply acts as a waste container. Lateral Flow Tests canoperate as either competitive or sandwich assays.

Immunohistochemical analysis: Immunoassays carried out in accordancewith some embodiments of the present invention may be homogeneous assaysor heterogeneous assays. In a homogeneous assay the immunologicalreaction usually involves the specific antibody (e.g., anti-TRAIL, CRPand/or IP-10 antibody), a labeled analyte, and the sample of interest.The signal arising from the label is modified, directly or indirectly,upon the binding of the antibody to the labeled analyte. Both theimmunological reaction and detection of the extent thereof can becarried out in a homogeneous solution. Immunochemical labels, which maybe employed, include free radicals, radioisotopes, fluorescent dyes,enzymes, bacteriophages, or coenzymes.

In a heterogeneous assay approach, the reagents are usually the sample,the antibody, and means for producing a detectable signal. Samples asdescribed above may be used. The antibody can be immobilized on asupport, such as a bead (such as protein A and protein G agarose beads),plate or slide, and contacted with the specimen suspected of containingthe antigen in a liquid phase.

According to a particular embodiment, the antibody is immobilized to aporous strip to form a detection site. The measurement or detectionregion of the porous strip may include a plurality of sites, one forTRAIL, one for CRP and one for IP-10. A test strip may also containsites for negative and/or positive controls.

Alternatively, control sites can be located on a separate strip from thetest strip. Optionally, the different detection sites may containdifferent amounts of antibodies, e.g., a higher amount in the firstdetection site and lesser amounts in subsequent sites. Upon the additionof test sample, the number of sites displaying a detectable signalprovides a quantitative indication of the amount of polypeptides presentin the sample. The detection sites may be configured in any suitablydetectable shape and are typically in the shape of a bar or dot spanningthe width of a test strip.

The support is then separated from the liquid phase and either thesupport phase or the liquid phase is examined for a detectable signalemploying means for producing such signal. The signal is related to thepresence of the analyte in the sample. Means for producing a detectablesignal include the use of radioactive labels, fluorescent labels, orenzyme labels. For example, if the antigen to be detected contains asecond binding site, an antibody which binds to that site can beconjugated to a detectable group and added to the liquid phase reactionsolution before the separation step. The presence of the detectablegroup on the solid support indicates the presence of the antigen in thetest sample. Examples of suitable immunoassays are oligonucleotides,immunoblotting, immunofluorescence methods, immunoprecipitation,chemiluminescence methods, electrochemiluminescence (ECL) orenzyme-linked immunoassays.

Those skilled in the art will be familiar with numerous specificimmunoassay formats and variations thereof which may be useful forcarrying out the method disclosed herein. See generally E. Maggio,Enzyme-Immunoassay, (1980) (CRC Press, Inc., Boca Raton, Fla.); see alsoU.S. Pat. No. 4,727,022 to Skold et al. titled “Methods for ModulatingLigand-Receptor Interactions and their Application,” U.S. Pat. No.4,659,678 to Forrest et al. titled “Immunoassay of Antigens,” U.S. Pat.No. 4,376,110 to David et al., titled “Immunometric Assays UsingMonoclonal Antibodies,” U.S. Pat. No. 4,275,149 to Litman et al., titled“Macromolecular Environment Control in Specific Receptor Assays,” U.S.Pat. No. 4,233,402 to Maggio et al., titled “Reagents and MethodEmploying Channeling,” and U.S. Pat. No. 4,230,767 to Boguslaski et al.,titled “Heterogenous Specific Binding Assay Employing a Coenzyme asLabel.”

Antibodies can be conjugated to a solid support suitable for adiagnostic assay (e.g., beads such as protein A or protein G agarose,microspheres, plates, slides or wells formed from materials such aslatex or polystyrene) in accordance with known techniques, such aspassive binding. Antibodies as described herein may likewise beconjugated to detectable labels or groups such as radiolabels (e.g.,³⁵S, ¹²⁵I, ¹³¹I) enzyme labels (e.g., horseradish peroxidase, alkalinephosphatase), and fluorescent labels (e.g., fluorescein, Alexa, greenfluorescent protein, rhodamine) in accordance with known techniques.

Monoclonal antibodies for measuring TRAIL include without limitation:Mouse, Monoclonal (55B709-3) IgG; Mouse, Monoclonal (2E5) IgG1; Mouse,Monoclonal (2E05) IgG1; Mouse, Monoclonal (M912292) IgG1 kappa; Mouse,Monoclonal (IIIF6) IgG2b; Mouse, Monoclonal (2E1-1B9) IgG1; Mouse,Monoclonal (RIK-2) IgG1, kappa; Mouse, Monoclonal M181 IgG1; Mouse,Monoclonal VI10E IgG2b; Mouse, Monoclonal MAB375 IgG1; Mouse, MonoclonalMAB687 IgG1; Mouse, Monoclonal HS501 IgG1; Mouse, Monoclonal clone75411.11 Mouse IgG1; Mouse, Monoclonal T8175-50 IgG; Mouse, Monoclonal2B2.108 IgG1; Mouse, Monoclonal B-T24 IgG1; Mouse, Monoclonal 55B709.3IgG1; Mouse, Monoclonal D3 IgG1; Goat, Monoclonal C19 IgG; Rabbit,Monoclonal H257 IgG; Mouse, Monoclonal 500-M49 IgG; Mouse, Monoclonal05-607 IgG; Mouse, Monoclonal B-T24 IgG1; Rat, Monoclonal (N2B2), IgG2a,kappa; Mouse, Monoclonal (1A7-2B7), IgG1; Mouse, Monoclonal (55B709.3),IgG and Mouse, Monoclonal B-S23* IgG1, Human TRAIL/TNFSF10 MAb (Clone75411), Mouse IgG1, Human TRAIL/TNFSF10 MAb (Clone 124723), Mouse IgG1,Human TRAIL/TNFSF10 MAb (Clone 75402), Mouse IgG1.

Antibodies for measuring TRAIL include monoclonal antibodies andpolyclonal antibodies for measuring TRAIL. Antibodies for measuringTRAIL include antibodies that were developed to target epitopes from thelist comprising of: Mouse myeloma cell line NS0-derived recombinanthuman TRAIL (Thr95-Gly281 Accession #P50591), Mouse myeloma cell line,NS0-derived recombinant human TRAIL (Thr95-Gly281, with an N-terminalMet and 6-His tag Accession #P50591), E. coli-derived, (Val114-Gly281,with and without an N-terminal Met Accession #: Q6IBA9), Human plasmaderived TRAIL, Human serum derived TRAIL, recombinant human TRAIL wherefirst amino acid is between position 85-151 and the last amino acid isat position 249-281.

Examples of monoclonal antibodies for measuring CRP include withoutlimitation: Mouse, Monoclonal (108-2A2); Mouse, Monoclonal (108-7G41D2);Mouse, Monoclonal (12D-2C-36), IgG1; Mouse, Monoclonal (1G1), IgG1;Mouse, Monoclonal (5A9), IgG2a kappa; Mouse, Monoclonal (63F4), IgG1;Mouse, Monoclonal (67A1), IgG1; Mouse, Monoclonal (8B-5E), IgG1; Mouse,Monoclonal (B893M), IgG2b, lambda; Mouse, Monoclonal (C1), IgG2b; Mouse,Monoclonal (C11F2), IgG; Mouse, Monoclonal (C2), IgG1; Mouse, Monoclonal(C3), IgG1; Mouse, Monoclonal (C4), IgG1; Mouse, Monoclonal (C5), IgG2a;Mouse, Monoclonal (C6), IgG2a; Mouse, Monoclonal (C7), IgG1; Mouse,Monoclonal (CRP103), IgG2b; Mouse, Monoclonal (CRP11), IgG1; Mouse,Monoclonal (CRP135), IgG1; Mouse, Monoclonal (CRP169), IgG2a; Mouse,Monoclonal (CRP30), IgG1; Mouse, Monoclonal (CRP36), IgG2a; Rabbit,Monoclonal (EPR283Y), IgG; Mouse, Monoclonal (KT39), IgG2b; Mouse,Monoclonal (N-a), IgG1; Mouse, Monoclonal (N1G1), IgG1; Monoclonal(P5A9AT); Mouse, Monoclonal (S5G1), IgG1; Mouse, Monoclonal (SB78c),IgG1; Mouse, Monoclonal (SB78d), IgG1 and Rabbit, Monoclonal (Y284),IgG, Human C-Reactive Protein/CRP Biot MAb (Cl 232024), Mouse IgG2B,Human C-Reactive Protein/CRP MAb (Clone 232007), Mouse IgG2B,Human/Mouse/Porcine C-Reactive Protein/CRP MAb (Cl 232026), Mouse IgG2A.

Antibodies for measuring CRP include monoclonal antibodies for measuringCRP and polyclonal antibodies for measuring CRP.

Antibodies for measuring CRP also include antibodies that were developedto target epitopes from the list comprising of: Human plasma derivedCRP, Human serum derived CRP, Mouse myeloma cell line NS0-derivedrecombinant human C-Reactive Protein/CRP (Phe17-Pro224 Accession#P02741).

Examples of monoclonal antibodies for measuring IP-10 include withoutlimitation: IP-10/CXCL10 Mouse anti-Human Monoclonal (4D5) Antibody(LifeSpan BioSciences), IP-10/CXCL10 Mouse anti-Human Monoclonal(A00163.01) Antibody (LifeSpan BioSciences), MOUSE ANTI HUMAN IP-10 (AbDSerotec), RABBIT ANTI HUMAN IP-10 (AbD Serotec), IP-10 Human mAb 6D4(Hycult Biotech), Mouse Anti-Human IP-10 Monoclonal Antibody Clone B-050(Diaclone), Mouse Anti-Human IP-10 Monoclonal Antibody Clone B-055(Diaclone), Human CXCL10/IP-10 MAb Clone 33036 (R&D Systems),CXCL10/INP10 Antibody 1E9 (Novus Biologicals), CXCL10/INP10 Antibody 2C1(Novus Biologicals), CXCL10/INP10 Antibody 6D4 (Novus Biologicals),CXCL10 monoclonal antibody M01A clone 2C1 (Abnova Corporation), CXCL10monoclonal antibody (M05), clone 1E9 (Abnova Corporation), CXCL10monoclonal antibody, clone 1 (Abnova Corporation), IP10 antibody 6D4(Abcam), IP10 antibody EPR7849 (Abcam), IP10 antibody EPR7850 (Abcam).

Antibodies for measuring IP-10 include monoclonal antibodies formeasuring IP-10 and polyclonal antibodies for measuring IP-10.

Antibodies for measuring IP-10 also include antibodies that weredeveloped to target epitopes from the list comprising of: Recombinanthuman CXCL10/IP-10, non-glycosylated polypeptide chain containing 77amino acids (aa 22-98) and an N-terminal His tag Interferon gammainducible protein 10 (125 aa long), IP-10 His Tag Human RecombinantIP-10 produced in E. coli containing 77 amino acids fragment (22-98) andhaving a total molecular mass of 8.5 kDa with an amino-terminalhexahistidine tag, E. coli-derived Human IP-10 (Val22-Pro98) with anN-terminal Met, Human plasma derived IP-10, Human serum derived IP-10,recombinant human IP-10 where first amino acid is between position 1-24and the last amino acid is at position 71-98.

It will be appreciated that the expression level of the polypeptidesdescribed herein can be an absolute expression level, a normalizedexpression and/or a relative expression level.

In general scientific context, normalization is a process by which ameasurement raw data is converted into data that may be directlycompared with other so normalized data. In the context of the presentinvention, measurements of expression levels are prone to errors causedby, for example, unequal degradation of measured samples, differentloaded quantities per assay, and other various errors. Morespecifically, any assayed sample may contain more or less biologicalmaterial than is intended, due to human error and equipment failures.Thus, the same error or deviation applies to both the polypeptide of theinvention and to the control reference, whose expression is essentiallyconstant. Thus, division of TRAIL, IP-10 or CRP raw expression value bythe control reference raw expression value yields a quotient which isessentially free from any technical failures or inaccuracies (except formajor errors which destroy the sample for testing purposes) andconstitutes a normalized expression value of the polypeptide. Sincecontrol reference expression values are equal in different samples, theyconstitute a common reference point that is valid for suchnormalization.

According to a particular embodiment, each of the polypeptide expressionvalues are normalized using the same control reference.

It will further be appreciated that absolute expression values aredependent upon the exact protocol used, since each protocol typicallyleads to different signal to noise ratios, and consequentially todifferent concentrations being measured. More specifically, while theoverall trend of the biomarkers will be preserved regardless of theprotocol (e.g. TRAIL increases in viral infections and decreases inbacterial), the measurement scale is protocol dependent.

Such alterations in measured concentrations of proteins across differentprotocols can be compensated for by correlating the measurements of thetwo protocols and computing a transformation function, as illustrated inExample 5 herein below.

Typically, the samples which are analyzed are blood sample comprisingwhole blood, serum, plasma, leukocytes or blood cells. Preferably, thesample is whole blood, serum or plasma.

Of note, TRAIL and IP-10 and CRP are highly expressed in other tissuesand samples including without limitation CSF, saliva and epithelialcells, bone marrow aspiration, urine, stool, alveolar lavage, sputum.Thus, some embodiments of the present invention can be used to measureTRAIL, CRP and IP-10 in such tissues and samples. Preferably, the levelof the polypeptides is measured within about 24 hours after the sampleis obtained. Alternatively, the concentration of the polypeptides ismeasured in a sample that was stored at 12° C. or lower, when storagebegins less than 24 hours after the sample is obtained.

Once the tests are carried out to determine the level of thepolypeptides, a subject specific dataset is optionally generated whichcontains the results of the measurements.

The subject-specific dataset may be stored in a computer readable formaton a non-volatile computer readable medium, and is optionally andpreferably accessed by a hardware processor, such as a general purposecomputer or dedicated circuitry.

As mentioned, the levels of the polypeptides in the test subjects bloodare compared to the levels of the identical polypeptides in a pluralityof subjects' blood, when the subjects have already been verified ashaving a bacterial infection, viral infection or non-bacterial/non-viraldisease on the basis of parameters other than the blood level of thepolypeptides. The levels of the polypeptides of the plurality ofsubjects together with their verified diagnosis can be stored in asecond dataset, also referred to herein as the “group dataset” or“prediagnosed dataset”, as further described herein below.

The phrase “non-bacterial/non-viral disease” refers to disease that isnot caused by a bacteria or virus. This includes diseases such as acutemyocardial infarction, physical injury, epileptic attack, inflammatorydisorders etc, fungal diseases, parasitic diseases etc.

The phrase “viral infection” as used herein refers to a disease that iscaused by a virus and does not comprise a bacterial component.

Methods of analyzing a dataset, for example, for the purpose ofcalculating one or more probabilistic classification functionrepresenting the likelihood that a particular subject has a bacterialinfection, or the likelihood that a particular subject has a viralinfection or the likelihood that a particular subject has anon-bacterial non-viral disease, may be performed as described inExample 1 herein below. For example, diagnosis may be supported usingPCR diagnostic assays such as (i) Seeplex® RV15 for detection ofparainfluenza virus 1, 2, 3, and 4, coronavirus 229E/NL63, adenovirusA/B/C/D/E, bocavirus 1/2/3/4, influenza virus A and B, metapneumovirus,coronavirus OC43, rhinovirus A/B/C, respiratory syncytial virus A and B,and Enterovirus, or (ii) Seeplex® PB6 for detection of Streptococcuspneumoniae, Haemophilus influenzae, Chlamydophila pneumoniae, Legionellapneumophila, Bordetella pertussis, and Mycoplasma pneumoniae.

Blood cultures, urine cultures and stool cultures may be analyzed forShigella spp., Campylobacter spp. and Salmonella spp.; serologicaltesting (IgM and/or IgG) for cytomegalovirus (CMV), Epstein-Barr virus(EBV), Mycoplasma Pneumonia, and Coxiella burnetii (Q-Fever).

Radiological tests (e.g. chest X-ray for suspected lower respiratorytract infection [LRTI]) may be used to confirm chest infections.

Alternatively, or additionally at least one trained physician may beused to establish the diagnosis.

Methods of determining the expression level of the polypeptides in thepre-diagnosed subjects have been described herein above.

Preferably, the same method which is used for determining the expressionlevel of the polypeptides in the pre-diagnosed subjects is used fordetermining the level of the polypeptides in the test subject. Thus, forexample if an immunoassay type method is used for determining theexpression level of the polypeptides in the pre-diagnosed subjects, thenan immunoassay type method should be used for determining the level ofthe polypeptides in the test subject.

It will be appreciated that, the type of blood sample need not beidentical in the test subject and the pre-diagnosed subjects. Thepresent inventors were able to show that serum and plasma levels forTRAIL are very similar. Thus, for example, if a serum sample is used fordetermining the expression level of the polypeptides in thepre-diagnosed subjects, then a plasma sample may be used for determiningthe level of the polypeptides in the test subject.

The group dataset is preferably stored in a computer readable format ona non-volatile computer readable medium, and is optionally andpreferably accessed by a hardware processor, such as a general purposecomputer or dedicated circuitry. Both datasets can be stored on the samemedium and are optionally and preferably accessed by the same hardwareprocessor.

In the subject-specific dataset, each entry can optionally andpreferably be described as a tuple (D, L) where D represents thepolypeptide in the dataset and L represents the blood level of thepolypeptide D. Thus, the dataset may be a two-dimensional dataset inwhich all the elements can be described by a vector in a two-dimensionalspace spanned by the polypeptide and respective response. In the groupdataset, each entry can be described as a tuple (S, G, D, L) where Srepresents the particular subject, G represents the diagnosis of thesubject S in the group dataset, D represents the polypeptide and Lrepresents blood level of the polypeptide D. Thus, the exemplifiedillustration is of a four-dimensional dataset in which all the elementscan be described by a vector in a four-dimensional space spanned by thesubjects, diagnosis, polypeptide and respective responses. Someembodiments of the present invention contemplate use of datasets ofhigher dimensions. Such datasets are described hereinafter.

The group dataset may optionally and preferably also include one or moreof, more preferably all, the entries of the subject-specific dataset. Inembodiments in which group dataset includes all the entries of thesubject-specific dataset, it is not necessary to use two separatedatasets, since the entire dataset is contained in one inclusivedataset. Yet, such an inclusive dataset is optionally and preferablyannotated in a manner that allows distinguishing between the portion ofthe inclusive dataset that is associated with the subject underanalysis, and the portion of the inclusive dataset that is associatedonly with the other subjects. In the context of the present disclosure,the portion of the inclusive dataset that is associated with the subjectunder analysis is referred to as the subject-specific dataset even whenit is not provided as a separate dataset. Similarly, the portion of theinclusive dataset that is associated only with the other subjects isreferred to as the group dataset even when it is not provided as aseparate dataset.

The group dataset preferably includes polypeptide levels of manysubjects (e.g., at least 10 subjects being prediagnosed as having aviral infection, at least 10 subjects being prediagnosed as having abacterial infection and at least 10 subjects being prediagnosed ashaving a non-bacterial/non-viral disease; or at least 20 subjects beingprediagnosed as having a viral infection, at least 20 subjects beingprediagnosed as having a bacterial infection and at least 20 subjectsbeing prediagnosed as having a non-bacterial/non-viral disease; or atleast 50 subjects being prediagnosed as having a viral infection, atleast 50 subjects being prediagnosed as having a bacterial infection andat least 50 subjects being prediagnosed as having anon-bacterial/non-viral disease.

The group-specific dataset can include additional data that describesthe subjects. Datasets that include additional data may be advantageoussince they provide additional information regarding the similaritiesbetween the subject under analysis and the other subject, therebyincreasing the accuracy of the predictability.

Representative examples of types of data other than the level of thepolypeptides include, without limitation traditional laboratory riskfactors and/or clinical parameters, as further described herein above.

The present embodiments contemplate subject-specific and group datasetsthat include additional data, aside from the polypeptides and respectivelevels. In some embodiments at least one of the datasets comprises oneor more (e.g., a plurality of) multidimensional entries, each entryhaving at least three dimensions, in some embodiments at least one ofthe datasets comprises one or more (e.g., a plurality of)multidimensional entries, each entry having at least four dimensions, insome embodiments at least one of the datasets comprises one or more(e.g., a plurality of) multidimensional entries, each entry having atleast five dimensions, and in some embodiments at least one of thedatasets comprises one or more (e.g., a plurality of) multidimensionalentries, each entry having more than five dimensions.

The additional dimensions of the datasets provides additionalinformation pertaining to the subject under analysis, to the othersubjects and/or to levels of polypeptides other than TRAIL, CRP andIP-10.

In some embodiments of the present invention the additional informationpertains to at least one of traditional laboratory risk factors,clinical parameters, blood chemistry and/or a genetic profile.

“Traditional laboratory risk factors” encompass biomarkers isolated orderived from subject samples and which are currently evaluated in theclinical laboratory and used in traditional global risk assessmentalgorithms, such as absolute neutrophil count (abbreviated ANC),absolute lymphocyte count (abbreviated ALC), white blood count(abbreviated WBC), neutrophil % (defined as the fraction of white bloodcells that are neutrophils and abbreviated Neu (%)), lymphocyte %(defined as the fraction of white blood cells that are lymphocytes andabbreviated Lym (%)), monocyte % (defined as the fraction of white bloodcells that are monocytes and abbreviated Mon (%)), Sodium (abbreviatedNa), Potassium (abbreviated K), Bilirubin (abbreviated Bili).

Preferably, at least one of the traditional laboratory risk factors ofthe subject under analysis is included in the subject specific dataset,and at least one of the traditional laboratory risk factors of one ormore (more preferably all) of the other subjects is included in thegroup dataset. When the subject specific dataset includes at least oneof the traditional laboratory risk factors, the risk factors can beincluded as a separate entry. When the group dataset includes the riskfactors, the risk factors is optionally and preferably included persubject. Thus, for example, a group dataset entry can be described bythe tuple (S, G, D, L {R}), where S, G, D and L have been introducedbefore and {R} is the at least one risk factor of subject S.

“Clinical parameters” encompass all non-sample or non-analyte biomarkersof subject health status or other characteristics, such as, withoutlimitation, age (Age), ethnicity (RACE), gender (Sex), core bodytemperature (abbreviated “temperature”), maximal core body temperaturesince initial appearance of symptoms (abbreviated “maximaltemperature”), time from initial appearance of symptoms (abbreviated“time from symptoms”), pregnancy, or family history (abbreviated FamHX).

Preferably, at least one of the clinical parameters of the subject underanalysis is included in the subject specific dataset, and at least oneof the clinical parameters of one or more (more preferably all) of theother subjects is included in the group dataset. When the subjectspecific dataset includes at least one of the clinical parameters, theclinical parameters can be included as a separate entry. When the groupdataset includes the clinical parameters, the clinical parameters isoptionally and preferably included per subject. Thus, for example, agroup dataset entry can be described by the tuple (S, G, D, L {C}),where S, G, D and L have been introduced before and {C} is the clinicalparameter of subject S.

As used herein “blood chemistry” refers to the concentration, orconcentrations, of any and all substances dissolved in, or comprising,the blood. Representative examples of such substances, include, withoutlimitation, albumin, amylase, alkaline phosphatase, bicarbonate, totalbilirubin, BUN, C-reactive protein, calcium, chloride, LDL, HDL, totalcholesterol, creatinine, CPK, γ-GT, glucose, LDH, inorganic phosphorus,lipase, potassium, total protein, AST, ALT, sodium, triglycerides, uricacid and VLDL.

According to one embodiment, the blood chemistry of the subject underanalysis is included in the subject specific dataset, and the bloodchemistry of one or more (more preferably all) of the other subjects isincluded in the group dataset. When the subject specific datasetincludes the blood chemistry, the blood chemistry can be included as aseparate entry. When the group dataset includes the blood chemistry, theblood chemistry is optionally and preferably included per subject. Thus,for example, a group dataset entry can be described by the tuple (S, G,D, L {C}), where S, G, D and L have been introduced before and {C} isthe blood chemistry of subject S.

In some embodiments of the present invention the additional informationpertains to a genetic profile of individual.

As used herein “genetic profile” refers to the analysis of a number ofdifferent genes. A genetic profile can encompass the genes in an entiregenome of the individual, or it can encompass a specific subset ofgenes. The genetic profile may include genomic profile, a proteomicprofile, an epigenomic profile and/or a transcriptomic profile.

Preferably, the genetic profile of the subject under analysis isincluded in the subject specific dataset, and the genetic profile of oneor more (more preferably all) of the other subjects is included in thegroup dataset. When the subject specific dataset includes the geneticprofile, the genetic profile can be included as a separate entry. Whenthe group dataset includes the genetic profile, the genetic profile isoptionally and preferably included per subject. Thus, for example, agroup dataset entry can be described by the tuple (S, G, D, L {P}),where S, G, D and L have been introduced before and {P} is the geneticprofile of subject S.

The method optionally and preferably continues to a step of storing thelevels of the polypeptide, at least temporarily, on a non-volatilecomputer readable medium from which it can be extracted or displayed asdesired.

Once the two datasets are accessed, the method continues to the analysisphase in order to diagnose the test subject.

The analysis is performed so as to compute one or more probabilisticclassification functions f(δ₀,δ₁), g(δ₀,δ₁), h(δ₀,δ₁), representing thelikelihoods that a particular subject has a bacterial infection, viralinfection or non-viral, non-bacterial disease, respectively. Typically,f, g and h satisfy the relation f(δ₀,δ₁)+g(δ₀,δ₁)+h(δ₀,δ₁)=1. Eachclassification function is a function of the first coordinate δ₀ and thesecond coordinate δ₁, wherein each of the coordinates δ₀ and δ₁ isdefined by a different combination of the expression values as furtherdetailed hereinabove.

The analysis can be executed in more than one way.

According to one embodiment, the analysis uses a binary or, morepreferably, trinary classifier to compute one or more of theprobabilistic classification functions.

Preferably, the analysis sums the probability of the viral and thenon-viral, non-bacterial disease in order to assign the likelihood of anon-bacterial infection. In another preferred embodiment, the analysissums the probability of the viral and bacterial to assign the likelihoodof an infectious disease. Yet in another preferred embodiment theanalysis ignores the probability of the non-viral, non-bacterialdisease, and performs a direct comparison of the bacterial and the viralprobabilities. Exemplified interpretation functions suitable foranalyzing the datasets according to some embodiments of the presentinvention are provided hereinunder.

The analysis of the datasets according to some embodiments of thepresent invention comprises executing a machine learning procedure.

As used herein the term “machine learning” refers to a procedureembodied as a computer program configured to induce patterns,regularities, or rules from previously collected data to develop anappropriate response to future data, or describe the data in somemeaningful way.

Use of machine learning is particularly, but not exclusively,advantageous when the dataset includes multidimensional entries.

The group and subject datasets can be used as a training set from whichthe machine learning procedure can extract parameters that best describethe dataset. Once the parameters are extracted, they can be used topredict the type of infection.

In machine learning, information can be acquired via supervised learningor unsupervised learning. In some embodiments of the invention themachine learning procedure comprises, or is, a supervised learningprocedure. In supervised learning, global or local goal functions areused to optimize the structure of the learning system. In other words,in supervised learning there is a desired response, which is used by thesystem to guide the learning.

In some embodiments of the invention the machine learning procedurecomprises, or is, an unsupervised learning procedure. In unsupervisedlearning there are typically no goal functions. In particular, thelearning system is not provided with a set of rules. One form ofunsupervised learning according to some embodiments of the presentinvention is unsupervised clustering in which the data objects are notclass labeled, a priori.

Representative examples of “machine learning” procedures suitable forthe present embodiments, including, without limitation, clustering,association rule algorithms, feature evaluation algorithms, subsetselection algorithms, support vector machines, classification rules,cost-sensitive classifiers, vote algorithms, stacking algorithms,Bayesian networks, decision trees, neural networks, instance-basedalgorithms, linear modeling algorithms, k-nearest neighbors analysis,ensemble learning algorithms, probabilistic models, graphical models,logistic regression methods (including multinomial logistic regressionmethods), gradient ascent methods, singular value decomposition methodsand principle component analysis. Among neural network models, theself-organizing map and adaptive resonance theory are commonly usedunsupervised learning algorithms. The adaptive resonance theory modelallows the number of clusters to vary with problem size and lets theuser control the degree of similarity between members of the sameclusters by means of a user-defined constant called the vigilanceparameter.

Following is an overview of some machine learning procedures suitablefor the present embodiments.

Association rule algorithm is a technique for extracting meaningfulassociation patterns among features.

The term “association”, in the context of machine learning, refers toany interrelation among features, not just ones that predict aparticular class or numeric value. Association includes, but it is notlimited to, finding association rules, finding patterns, performingfeature evaluation, performing feature subset selection, developingpredictive models, and understanding interactions between features.

The term “association rules” refers to elements that co-occur frequentlywithin the datasets. It includes, but is not limited to associationpatterns, discriminative patterns, frequent patterns, closed patterns,and colossal patterns.

A usual primary step of association rule algorithm is to find a set ofitems or features that are most frequent among all the observations.Once the list is obtained, rules can be extracted from them.

The aforementioned self-organizing map is an unsupervised learningtechnique often used for visualization and analysis of high-dimensionaldata. Typical applications are focused on the visualization of thecentral dependencies within the data on the map. The map generated bythe algorithm can be used to speed up the identification of associationrules by other algorithms. The algorithm typically includes a grid ofprocessing units, referred to as “neurons”. Each neuron is associatedwith a feature vector referred to as observation. The map attempts torepresent all the available observations with optimal accuracy using arestricted set of models. At the same time the models become ordered onthe grid so that similar models are close to each other and dissimilarmodels far from each other. This procedure enables the identification aswell as the visualization of dependencies or associations between thefeatures in the data.

Feature evaluation algorithms are directed to the ranking of features orto the ranking followed by the selection of features based on theirimpact.

The term “feature” in the context of machine learning refers to one ormore raw input variables, to one or more processed variables, or to oneor more mathematical combinations of other variables, including rawvariables and processed variables. Features may be continuous ordiscrete.

Information gain is one of the machine learning methods suitable forfeature evaluation. The definition of information gain requires thedefinition of entropy, which is a measure of impurity in a collection oftraining instances. The reduction in entropy of the target feature thatoccurs by knowing the values of a certain feature is called informationgain. Information gain may be used as a parameter to determine theeffectiveness of a feature in explaining the type of infection.Symmetrical uncertainty is an algorithm that can be used by a featureselection algorithm, according to some embodiments of the presentinvention. Symmetrical uncertainty compensates for information gain'sbias towards features with more values by normalizing features to a[0,1] range.

Subset selection algorithms rely on a combination of an evaluationalgorithm and a search algorithm. Similarly to feature evaluationalgorithms, subset selection algorithms rank subsets of features. Unlikefeature evaluation algorithms, however, a subset selection algorithmsuitable for the present embodiments aims at selecting the subset offeatures with the highest impact on the type of infection, whileaccounting for the degree of redundancy between the features included inthe subset. The benefits from feature subset selection includefacilitating data visualization and understanding, reducing measurementand storage requirements, reducing training and utilization times, andeliminating distracting features to improve classification.

Two basic approaches to subset selection algorithms are the process ofadding features to a working subset (forward selection) and deletingfrom the current subset of features (backward elimination). In machinelearning, forward selection is done differently than the statisticalprocedure with the same name. The feature to be added to the currentsubset in machine learning is found by evaluating the performance of thecurrent subset augmented by one new feature using cross-validation. Inforward selection, subsets are built up by adding each remaining featurein turn to the current subset while evaluating the expected performanceof each new subset using cross-validation. The feature that leads to thebest performance when added to the current subset is retained and theprocess continues. The search ends when none of the remaining availablefeatures improves the predictive ability of the current subset. Thisprocess finds a local optimum set of features.

Backward elimination is implemented in a similar fashion. With backwardelimination, the search ends when further reduction in the feature setdoes not improve the predictive ability of the subset. The presentembodiments contemplate search algorithms that search forward, backwardor in both directions. Representative examples of search algorithmssuitable for the present embodiments include, without limitation,exhaustive search, greedy hill-climbing, random perturbations ofsubsets, wrapper algorithms, probabilistic race search, schemata search,rank race search, and Bayesian classifier.

A decision tree is a decision support algorithm that forms a logicalpathway of steps involved in considering the input to make a decision.

The term “decision tree” refers to any type of tree-based learningalgorithms, including, but not limited to, model trees, classificationtrees, and regression trees.

A decision tree can be used to classify the datasets or their relationhierarchically. The decision tree has tree structure that includesbranch nodes and leaf nodes. Each branch node specifies an attribute(splitting attribute) and a test (splitting test) to be carried out onthe value of the splitting attribute, and branches out to other nodesfor all possible outcomes of the splitting test. The branch node that isthe root of the decision tree is called the root node. Each leaf nodecan represent a classification (e.g., whether a particular portion ofthe group dataset matches a particular portion of the subject-specificdataset) or a value. The leaf nodes can also contain additionalinformation about the represented classification such as a confidencescore that measures a confidence in the represented classification(i.e., the likelihood of the classification being accurate). Forexample, the confidence score can be a continuous value ranging from 0to 1, which a score of 0 indicating a very low confidence (e.g., theindication value of the represented classification is very low) and ascore of 1 indicating a very high confidence (e.g., the representedclassification is almost certainly accurate).

Support vector machines are algorithms that are based on statisticallearning theory. A support vector machine (SVM) according to someembodiments of the present invention can be used for classificationpurposes and/or for numeric prediction. A support vector machine forclassification is referred to herein as “support vector classifier,”support vector machine for numeric prediction is referred to herein as“support vector regression”.

An SVM is typically characterized by a kernel function, the selection ofwhich determines whether the resulting SVM provides classification,regression or other functions. Through application of the kernelfunction, the SVM maps input vectors into high dimensional featurespace, in which a decision hyper-surface (also known as a separator) canbe constructed to provide classification, regression or other decisionfunctions. In the simplest case, the surface is a hyper-plane (alsoknown as linear separator), but more complex separators are alsocontemplated and can be applied using kernel functions. The data pointsthat define the hyper-surface are referred to as support vectors.

The support vector classifier selects a separator where the distance ofthe separator from the closest data points is as large as possible,thereby separating feature vector points associated with objects in agiven class from feature vector points associated with objects outsidethe class. For support vector regression, a high-dimensional tube with aradius of acceptable error is constructed which minimizes the error ofthe data set while also maximizing the flatness of the associated curveor function. In other words, the tube is an envelope around the fitcurve, defined by a collection of data points nearest the curve orsurface.

An advantage of a support vector machine is that once the supportvectors have been identified, the remaining observations can be removedfrom the calculations, thus greatly reducing the computationalcomplexity of the problem. An SVM typically operates in two phases: atraining phase and a testing phase. During the training phase, a set ofsupport vectors is generated for use in executing the decision rule.During the testing phase, decisions are made using the decision rule. Asupport vector algorithm is a method for training an SVM. By executionof the algorithm, a training set of parameters is generated, includingthe support vectors that characterize the SVM. A representative exampleof a support vector algorithm suitable for the present embodimentsincludes, without limitation, sequential minimal optimization.

Regression techniques which may be used in accordance with the presentinvention include, but are not limited to linear Regression, MultipleRegression, logistic regression, probit regression, ordinal logisticregression ordinal Probit-Regression, Poisson Regression, negativebinomial Regression, multinomial logistic Regression (MLR) and truncatedregression.

A logistic regression or logit regression is a type of regressionanalysis used for predicting the outcome of a categorical dependentvariable (a dependent variable that can take on a limited number ofvalues, whose magnitudes are not meaningful but whose ordering ofmagnitudes may or may not be meaningful) based on one or more predictorvariables. Logistic regressions also include a multinomial variant. Themultinomial logistic regression model, is a regression model whichgeneralizes logistic regression by allowing more than two discreteoutcomes. That is, it is a model that is used to predict theprobabilities of the different possible outcomes of a categoricallydistributed dependent variable, given a set of independent variables(which may be real-valued, binary-valued, categorical-valued, etc.).

The advantage of logistic regression is that it assigns an interpretablemeasure of prediction confidence—a probability. For example, patientspredicted of having a bacterial infection with a probability of 75% and99%, would both be assigned as bacterial when using an SVMinterpretation function but the fact that the latter has a higherprobability would be masked. Assigning the likelihood level ofconfidence adds valuable clinical information that may affect clinicaljudgment.

Importantly, calculating the likelihood of infection type for eachpatients, allows to rationally filter out patients for which the systemknows that it cannot classify with high certainty. This is demonstratedin FIG. 5 , herein. Thus, when the product assigns a likelihood of say40% bacterial infection (40 out of 100 patients with the “40%” scorewill be bacterial).

Additionally, by using thresholds on the likelihood scores, one canassign non-binary classifications of the test-subject. By way of examplea test-subject with a bacterial likelihood below 30% can be assigned alow probability of bacterial infection; between 30% and 70% anintermediate probability of bacterial infection and above 70% a highprobability of a bacterial infections. Other thresholds may be used.

The Least Absolute Shrinkage and Selection Operator (LASSO) algorithm isa shrinkage and/or selection algorithm for linear regression. The LASSOalgorithm may minimizes the usual sum of squared errors, with aregularization, that can be an L1 norm regularization (a bound on thesum of the absolute values of the coefficients), an L2 normregularization (a bound on the sum of squares of the coefficients), andthe like. The LASSO algorithm may be associated with soft-thresholdingof wavelet coefficients, forward stagewise regression, and boostingmethods. The LASSO algorithm is described in the paper: Tibshirani, R,Regression Shrinkage and Selection via the Lasso, J. Royal. Statist. SocB., Vol. 58, No. 1, 1996, pages 267-288, the disclosure of which isincorporated herein by reference.

A Bayesian network is a model that represents variables and conditionalinterdependencies between variables. In a Bayesian network variables arerepresented as nodes, and nodes may be connected to one another by oneor more links. A link indicates a relationship between two nodes. Nodestypically have corresponding conditional probability tables that areused to determine the probability of a state of a node given the stateof other nodes to which the node is connected. In some embodiments, aBayes optimal classifier algorithm is employed to apply the maximum aposteriori hypothesis to a new record in order to predict theprobability of its classification, as well as to calculate theprobabilities from each of the other hypotheses obtained from a trainingset and to use these probabilities as weighting factors for futurepredictions of the type of infection. An algorithm suitable for a searchfor the best Bayesian network, includes, without limitation, globalscore metric-based algorithm. In an alternative approach to building thenetwork, Markov blanket can be employed. The Markov blanket isolates anode from being affected by any node outside its boundary, which iscomposed of the node's parents, its children, and the parents of itschildren.

Instance-based algorithms generate a new model for each instance,instead of basing predictions on trees or networks generated (once) froma training set.

The term “instance”, in the context of machine learning, refers to anexample from a dataset.

Instance-based algorithms typically store the entire dataset in memoryand build a model from a set of records similar to those being tested.This similarity can be evaluated, for example, through nearest-neighboror locally weighted methods, e.g., using Euclidian distances. Once a setof records is selected, the final model may be built using severaldifferent algorithms, such as the naive Bayes.

The present invention can also be used to screen patient or subjectpopulations in any number of settings. For example, a health maintenanceorganization, public health entity or school health program can screen agroup of subjects to identify those requiring interventions, asdescribed above, or for the collection of epidemiological data.Insurance companies (e.g., health, life or disability) may screenapplicants in the process of determining coverage or pricing, orexisting clients for possible intervention. Data collected in suchpopulation screens, particularly when tied to any clinical progressionto conditions like infection, will be of value in the operations of, forexample, health maintenance organizations, public health programs andinsurance companies. Such data arrays or collections can be stored inmachine-readable media and used in any number of health-related datamanagement systems to provide improved healthcare services, costeffective healthcare, improved insurance operation, etc. See, forexample, U.S. Patent Application No. 2002/0038227; U.S. PatentApplication No. US 2004/0122296; U.S. Patent Application No. US2004/0122297; and U.S. Pat. No. 5,018,067. Such systems can access thedata directly from internal data storage or remotely from one or moredata storage sites as further detailed herein.

A machine-readable storage medium can comprise a data storage materialencoded with machine readable data or data arrays which, when using amachine programmed with instructions for using said data, is capable ofuse for a variety of purposes. Measurements of effective amounts of thebiomarkers of the invention and/or the resulting evaluation of risk fromthose biomarkers can implemented in computer programs executing onprogrammable computers, comprising, inter alia, a processor, a datastorage system (including volatile and non-volatile memory and/orstorage elements), at least one input device, and at least one outputdevice.

Program code can be applied to input data to perform the functionsdescribed above and generate output information. The output informationcan be applied to one or more output devices, according to methods knownin the art. The computer may be, for example, a personal computer,microcomputer, or workstation of conventional design.

Each program can be implemented in a high level procedural or objectoriented programming language to communicate with a computer system.However, the programs can be implemented in assembly or machinelanguage, if desired. The language can be a compiled or interpretedlanguage. Each such computer program can be stored on a storage media ordevice (e.g., ROM or magnetic diskette or others as defined elsewhere inthis disclosure) readable by a general or special purpose programmablecomputer, for configuring and operating the computer when the storagemedia or device is read by the computer to perform the proceduresdescribed herein.

The health-related data management system of the invention may also beconsidered to be implemented as a computer-readable storage medium,configured with a computer program, where the storage medium soconfigured causes a computer to operate in a specific and predefinedmanner to perform various functions described herein.

The recorded output may include the assay results, findings, diagnoses,predictions and/or treatment recommendations. These may be communicatedto technicians, physicians and/or patients, for example. In certainembodiments, computers will be used to communicate such information tointerested parties, such as, patients and/or the attending physicians.Based on the output, the therapy administered to a subject can bemodified.

In one embodiment, the output is presented graphically. In anotherembodiment, the output is presented numerically (e.g. as a probability).In another embodiment, the output is generated using a color index (forexample in a bar display) where one color indicates bacterial infectionand another color non-bacterial infection. The strength of the colorcorrelates with the probability of bacterial infection/non-infection.Such a graphic display is presented in FIGS. 29A-29F.

In some embodiments, the output is communicated to the subject as soonas possible after the assay is completed and the diagnosis and/orprediction is generated. The results and/or related information may becommunicated to the subject by the subject's treating physician.Alternatively, the results may be communicated directly to a testsubject by any means of communication, including writing, such as byproviding a written report, electronic forms of communication, such asemail, or telephone. Communication may be facilitated by use of acomputer, such as in case of email communications. In certainembodiments, the communication containing results of a diagnostic testand/or conclusions drawn from and/or treatment recommendations based onthe test, may be generated and delivered automatically to the subjectusing a combination of computer hardware and software which will befamiliar to artisans skilled in telecommunications. One example of ahealthcare-oriented communications system is described in U.S. Pat. No.6,283,761; however, the present disclosure is not limited to methodswhich utilize this particular communications system. In certainembodiments of the methods of the disclosure, all or some of the methodsteps, including the assaying of samples, diagnosing of diseases, andcommunicating of assay results or diagnoses, may be carried out indiverse (e.g., foreign) jurisdictions.

In some embodiments, the methods described herein are carried out usinga system 330, which optionally and preferably, but not necessarily,comprises a hand-held device, which comprises at least two compartmentsthe first which measures the amount of polypeptides in the blood (e.g.using an immunohistochemical method) and the second whichcomputationally analyzes the results measured in the first compartmentand provides an output relating to the diagnosis.

A block diagram of representative example of system 330 according tosome embodiments of the present invention is illustrated in FIG. 34 .System 330 can comprise a device 331 which can be, but is notnecessarily a hand-held device. Alternatively, device 331 which can be adesktop mountable or a desktop placeable device. System 330 can comprisea first compartment 332 having a measuring system 333 configured tomeasure the expression value of the polypeptides in the blood of asubject. Measuring system 333 can perform at least one automated assayselected from the group consisting of an automated ELISA, an automatedimmunoassay, and an automated functional assay. System 330 can alsocomprise a second compartment 334 comprising a hardware processor 336having a computer-readable medium 338 for storing computer programinstructions for executing the operations described herein (e.g.,computer program instructions for defining the first and/or secondcoordinates, computer program instructions for defining the curved lineand/or plane, computer program instructions for calculating the firstand/or distances, computer program instructions for correlating thecalculated distance(s) to the presence of, absence of, or likelihoodthat the subject has, a bacterial and/or viral infection). Hardwareprocessor 336 is configured to receive expression value measurementsfrom first compartment 332 and execute the program instructionsresponsively to the measurements. Optionally and preferably hardwareprocessor 336 is also configured to output the processed data to adisplay device 340.

In some optional embodiments of the present invention, system 330communicates with a communication network. In these embodiments, system330 or hardware processor 336 comprises a network interface 350 thatcommunicates with a communication network 352. In the representativeillustration shown in FIG. 34 , network 352 is used for transmitting theresults of the analysis performed by hardware processor 336 (forexample, the presence of, absence of, or likelihood that the subjecthas, a bacterial and/or viral infection) to one or more remotelocations. For example, system 330 can transmit the analysis results toat least one of a laboratory information system 360, and/or a centralserver 362 that collects data from a plurality of systems like system330.

FIG. 39A is a schematic illustration showing a block diagram of system330 in embodiments in which communication network 352 is used forreceiving expression value measurements. In these embodiments, system330 can comprise computer-readable medium 338, as further detailedhereinabove, and a hardware processor, such as, but not limited to,processor 336. Hardware processor 336 can comprise network interface350. Via interface 350, hardware processor 336 receives expression valuemeasurements from a measuring system, such as, but not limited to,measuring system 333, and executes the computer program instructions incomputer-readable medium 338, responsively to the received measurements.Hardware processor 336 can then output the processed data to displaydevice 340.

Combinations of the embodiments shown in FIGS. 34 and 39A are alsocontemplated. For example, interface 350 can be used both for receivingexpression value measurements from network 352 and for transmitting theresults of the analysis to network 352.

In some embodiments of the present invention system 330 communicateswith a user, as schematically illustrated in the block diagram of FIG.39B. In these embodiments, system 330 can comprise computer-readablemedium 338, as further detailed hereinabove, and a hardware processor,such as, but not limited to, processor 336. Hardware processor 336comprises a user interface 354 that communicates with a user 356. Viainterface 350, hardware processor 336 receives expression valuemeasurements from user 356. User 356 can obtain the expression valuefrom an external source, or by executing at least one assay selectedfrom the group consisting of an immunoassay and a functional assay, orby operating system 333 (not shown, see FIGS. 39A and 34 ). Hardwareprocessor 336 executes the computer program instructions incomputer-readable medium 338, responsively to the received measurements.Hardware processor 336 can then output the processed data to displaydevice 340.

Once the diagnosis has been made, it will be appreciated that a numberof actions may be taken.

Thus, for example, if a bacterial infection is ruled in, then thesubject may be treated with an antibiotic agent.

Examples of antibiotic agents include, but are not limited toDaptomycin; Gemifloxacin; Telavancin; Ceftaroline; Fidaxomicin;Amoxicillin; Ampicillin; Bacampicillin; Carbenicillin; Cloxacillin;Dicloxacillin; Flucloxacillin; Mezlocillin; Nafcillin; Oxacillin;Penicillin G; Penicillin V; Piperacillin; Pivampicillin; Pivmecillinam;Ticarcillin; Aztreonam; Imipenem; Doripenem; Meropenem; Ertapenem;Clindamycin; Lincomycin; Pristinamycin; Quinupristin; Cefacetrile(cephacetrile); Cefadroxil (cefadroxyl); Cefalexin (cephalexin);Cefaloglycin (cephaloglycin); Cefalonium (cephalonium); Cefaloridine(cephaloradine); Cefalotin (cephalothin); Cefapirin (cephapirin);Cefatrizine; Cefazaflur; Cefazedone; Cefazolin (cephazolin); Cefradine(cephradine); Cefroxadine; Ceftezole; Cefaclor; Cefamandole;Cefmetazole; Cefonicid; Cefotetan; Cefoxitin; Cefprozil (cefproxil);Cefuroxime; Cefuzonam; Cefcapene; Cefdaloxime; Cefdinir; Cefditoren;Cefetamet; Cefixime; Cefmenoxime; Cefodizime; Cefotaxime; Cefpimizole;Cefpodoxime; Cefteram; Ceftibuten; Ceftiofur; Ceftiolene; Ceftizoxime;Ceftriaxone; Cefoperazone; Ceftazidime; Cefclidine; Cefepime;Cefluprenam; Cefoselis; Cefozopran; Cefpirome; Cefquinome; FifthGeneration; Ceftobiprole; Ceftaroline; Not Classified; Cefaclomezine;Cefaloram; Cefaparole; Cefcanel; Cefedrolor; Cefempidone; Cefetrizole;Cefivitril; Cefmatilen; Cefmepidium; Cefovecin; Cefoxazole; Cefrotil;Cefsumide; Cefuracetime; Ceftioxide; Azithromycin; Erythromycin;Clarithromycin; Dirithromycin; Roxithromycin; Telithromycin; Amikacin;Gentamicin; Kanamycin; Neomycin; Netilmicin; Paromomycin; Streptomycin;Tobramycin; Flumequine; Nalidixic acid; Oxolinic acid; Piromidic acid;Pipemidic acid; Rosoxacin; Ciprofloxacin; Enoxacin; Lomefloxacin;Nadifloxacin; Norfloxacin; Ofloxacin; Pefloxacin; Rufloxacin;Balofloxacin; Gatifloxacin; Grepafloxacin; Levofloxacin; Moxifloxacin;Pazufloxacin; Sparfloxacin; Temafloxacin; Tosufloxacin; Besifloxacin;Clinafloxacin; Gemifloxacin; Sitafloxacin; Trovafloxacin; Prulifloxacin;Sulfamethizole; Sulfamethoxazole; Sulfisoxazole;Trimethoprim-Sulfamethoxazole; Demeclocycline; Doxycycline; Minocycline;Oxytetracycline; Tetracycline; Tigecycline; Chloramphenicol;Metronidazole; Tinidazole; Nitrofurantoin; Vancomycin; Teicoplanin;Telavancin; Linezolid; Cycloserine 2; Rifampin; Rifabutin; Rifapentine;Bacitracin; Polymyxin B; Viomycin; Capreomycin.

If a viral infection is ruled in, the subject may be treated with anantiviral agent. Examples of antiviral agents include, but are notlimited to Abacavir; Aciclovir; Acyclovir; Adefovir; Amantadine;Amprenavir; Ampligen; Arbidol; Atazanavir; Atripla; Balavir;Boceprevirertet; Cidofovir; Combivir; Dolutegravir; Darunavir;Delavirdine; Didanosine; Docosanol; Edoxudine; Efavirenz; Emtricitabine;Enfuvirtide; Entecavir; Ecoliever; Famciclovir; Fomivirsen;Fosamprenavir; Foscarnet; Fosfonet; Fusion inhibitor; Ganciclovir;Ibacitabine; Imunovir; Idoxuridine; Imiquimod; Indinavir; Inosine;Integrase inhibitor; Interferon type III; Interferon type II; Interferontype I; Interferon; Lamivudine; Lopinavir; Loviride; Maraviroc;Moroxydine; Methisazone; Nelfinavir; Nevirapine; Nexavir; Oseltamivir;Peginterferon alfa-2a; Penciclovir; Peramivir; Pleconaril;Podophyllotoxin; Raltegravir; Reverse transcriptase inhibitor;Ribavirin; Rimantadine; Ritonavir; Pyramidine; Saquinavir; Sofosbuvir;StavudineTelaprevir; Tenofovir; Tenofovir disoproxil; Tipranavir;Trifluridine; Trizivir; Tromantadine; Truvada; traporved; Valaciclovir;Valganciclovir; Vicriviroc; Vidarabine; Viramidine; Zalcitabine;Zanamivir; Zidovudine; RNAi antivirals; inhaled rhibovirons; monoclonalantibody respigams; neuriminidase blocking agents.

The information gleaned using the methods described herein may aid inadditional patient management options. For example, the information maybe used for determining whether a patient should or should not beadmitted to hospital. It may also affect whether or not to prolonghospitalization duration. It may also affect the decision whetheradditional tests need to be performed or may save performing unnecessarytests such as CT and/or X-rays and/or MRI and/or culture and/or serologyand/or PCR assay for specific bacteria and/or PCR assays for virusesand/or perform procedures such as lumbar puncture.

It is often clinically useful to assess patient prognosis, diseaseseverity and outcome. The present inventors have now found that lowlevels of TRAIL (lower than about 20 pg/ml or about 15 pg/ml or about 10pg/ml or about 5 pg/ml or about 2 pg/ml) are significantly correlatedwith poor patient prognosis and outcome, and high disease severity. Forexample, the present inventors showed that adult patients in theintensive care unit (ICU), which are generally severely ill, hadsignificantly lower TRAIL levels compared to all other patients, whichwere less ill regardless of whether they had an infectious ornon-infectious etiology.

Thus, according to another aspect of the present invention there isprovided a method of predicting a prognosis for a disease comprisingmeasuring the TRAIL protein serum level in subject having the disease,wherein when the TRAIL level is below a predetermined level, theprognosis is poorer than for a subject having a disease having a TRAILprotein serum level above the predetermined level.

Methods of measuring TRAIL protein serum levels are described hereinabove.

The disease may be an infectious disease or a non-infectious disease.The subject may have a disease which has been diagnosed ornon-diagnosed.

Particular examples of diseases include without limitation bacterialinfections (e.g. bacteremia, meningitis, respiratory tract infections,urinal tract infections etc.), sepsis, physical injury and trauma,cardiovascular diseases, multi-organ failure associated diseases,drug-induced nephrotoxicity, acute kidney disease, renal injury,advanced cirrhosis and liver failure, acute or chronic left heartfailure, pulmonary hypertension with/without right heart failure, andvarious types of malignancies.

According to another embodiment, additional polypeptides are measuredwhich aid in increasing the accuracy of the prediction. Thus, forexample, other polypeptide which may be measured include IP-10, CRP,IL1RA, PCT and SAA.

According to a particular embodiment, IP-10, CRP and TRAIL are measured.

According to another embodiment, only TRAIL is measured.

The present inventors have found that patients having very low levels ofTRAIL (as classified herein above) have lower chance of recovery, andhigher chance of complications. Accordingly, the present inventorspropose that when it is found that a subject has very low levels ofTRAIL they should be treated with agents that are only used as a lastresort.

Such agents for example may be for example experimental agents that havenot been given full FDA approval. Other last resort agents are thosethat are known to be associated with severe side effects. Anotherexemplary last resort agent is an antibiotic such as vancomycin (whichis typically not provided so as to prevent the spread of antibioticresistance).

It will be appreciated that agents that are not typically considered aslast resort agents can also be provided, but in doses which exceed theclinically acceptable dose.

According to this aspect of the present invention, if the TRAIL level isabove a predetermined level, then the patient should typically not betreated with a last resort agent.

The present inventors have now found that basal levels of TRAIL inhealthy individuals or patients with a non-infectious disease are lowerin females compared to males during fertility age (t-test P<0.001) (seeFIG. 37A), but is invariant in pre- or post-fertility age (t-test P=0.9,FIG. 37A). This trend was not observed in patients with an infectiousdisease.

This age dependent dynamics can be used to improve models distinguishingbetween bacterial, viral and non-infectious or healthy individuals, aswould be evident to one skilled in the art.

For example, the model can include age and gender parameters. If thesubject's age is within a certain range indicative of fertility (e.g.about 13 to 45 years) and the subject is male, then TRAIL modelcoefficients of males at fertility age can be used. If the subject's ageis within the range indicative of fertility and the subject is femalethen TRAIL model coefficients of females at fertility age can be used.If the subject's age is outside the range indicative of fertility thenTRAIL model coefficients that are gender invariant can be used.

Thus, according to another aspect of the invention there is provided amethod of determining an infection type in a female subject of fertilityage, the method comprising comparing the TRAIL protein serum level inthe subject to a predetermined threshold, said predetermined thresholdcorresponding to the TRAIL protein serum level of a healthy femalesubject of fertility age, or a group of healthy female subjects offertility age, wherein a difference between said TRAIL protein serumlevel and said predetermined threshold is indicative of an infectiontype.

Thus, according to another aspect of the invention there is provided amethod of determining an infection type in a male subject of fertilityage, the method comprising comparing the TRAIL protein serum level inthe subject to a predetermined threshold, said predetermined thresholdcorresponding to the TRAIL protein serum level of a healthy male subjectof fertility age, or a group of healthy male subjects of fertility age,wherein a difference between said TRAIL protein serum level and saidpredetermined threshold is indicative of an infection type.

It will be appreciated that predetermined thresholds can be used toeither rule in or rule out an infection type.

Thus, for example if the TRAIL protein serum level is above a firstpredetermined threshold, the infection type is viral.

If, for example the TRAIL protein serum level is above a secondpredetermined threshold, the infection type is not bacterial.

If for example, the TRAIL protein serum level is below a thirdpredetermined threshold, the infection type is bacterial.

If for example the TRAIL protein serum level is below a fourthpredetermined threshold, the infection type is not viral.

Typically, the healthy male or female subject, referred to herein has noknown disease. According to a particular embodiment, the control subjecthas no infectious disease.

Typically, the difference between the TRAIL protein serum level of thesubject and the predetermined threshold is a statistically significantdifference, as further described herein above.

As used herein the term “about” refers to ±10%.

The terms “comprises”, “comprising”, “includes”, “including”, “having”and their conjugates mean “including but not limited to”.

The term “consisting of” means “including and limited to”.

The term “consisting essentially of” means that the composition, methodor structure may include additional ingredients, steps and/or parts, butonly if the additional ingredients, steps and/or parts do not materiallyalter the basic and novel characteristics of the claimed composition,method or structure.

Throughout this application, various embodiments of this invention maybe presented in a range format. It should be understood that thedescription in range format is merely for convenience and brevity andshould not be construed as an inflexible limitation on the scope of theinvention. Accordingly, the description of a range should be consideredto have specifically disclosed all the possible subranges as well asindividual numerical values within that range. For example, descriptionof a range such as from 1 to 6 should be considered to have specificallydisclosed subranges such as from 1 to 3, from 1 to 4, from 1 to 5, from2 to 4, from 2 to 6, from 3 to 6 etc., as well as individual numberswithin that range, for example, 1, 2, 3, 4, 5, and 6. This appliesregardless of the breadth of the range.

As used herein the term “method” refers to manners, means, techniquesand procedures for accomplishing a given task including, but not limitedto, those manners, means, techniques and procedures either known to, orreadily developed from known manners, means, techniques and proceduresby practitioners of the chemical, pharmacological, biological,biochemical and medical arts.

As used herein, the term “treating” includes abrogating, substantiallyinhibiting, slowing or reversing the progression of a condition,substantially ameliorating clinical or aesthetical symptoms of acondition or substantially preventing the appearance of clinical oraesthetical symptoms of a condition.

It is appreciated that certain features of the invention, which are, forclarity, described in the context of separate embodiments, may also beprovided in combination in a single embodiment. Conversely, variousfeatures of the invention, which are, for brevity, described in thecontext of a single embodiment, may also be provided separately or inany suitable subcombination or as suitable in any other describedembodiment of the invention. Certain features described in the contextof various embodiments are not to be considered essential features ofthose embodiments, unless the embodiment is inoperative without thoseelements.

Various embodiments and aspects of the present invention as delineatedhereinabove and as claimed in the claims section below find experimentalsupport in the following examples.

EXAMPLES

Reference is now made to the following examples, which together with theabove descriptions illustrate some embodiments of the invention in a nonlimiting fashion. Generally, the nomenclature used herein and thelaboratory procedures utilized in the present invention includemolecular, biochemical, microbiological and recombinant DNA techniques.Such techniques are thoroughly explained in the literature. See, forexample, “Molecular Cloning: A laboratory Manual” Sambrook et al.,(1989); “Current Protocols in Molecular Biology” Volumes I-III Ausubel,R. M., ed. (1994); Ausubel et al., “Current Protocols in MolecularBiology”, John Wiley and Sons, Baltimore, Md. (1989); Perbal, “APractical Guide to Molecular Cloning”, John Wiley & Sons, New York(1988); Watson et al., “Recombinant DNA”, Scientific American Books, NewYork; Birren et al. (eds) “Genome Analysis: A Laboratory Manual Series”,Vols. 1-4, Cold Spring Harbor Laboratory Press, New York (1998);methodologies as set forth in U.S. Pat. Nos. 4,666,828; 4,683,202;4,801,531; 5,192,659 and 5,272,057; “Cell Biology: A LaboratoryHandbook”, Volumes I-III Cellis, J. E., ed. (1994); “Culture of AnimalCells—A Manual of Basic Technique” by Freshney, Wiley-Liss, N. Y.(1994), Third Edition; “Current Protocols in Immunology” Volumes I-IIIColigan J. E., ed. (1994); Stites et al. (eds), “Basic and ClinicalImmunology” (8th Edition), Appleton & Lange, Norwalk, Conn. (1994);Mishell and Shiigi (eds), “Selected Methods in Cellular Immunology”, W.H. Freeman and Co., New York (1980); available immunoassays areextensively described in the patent and scientific literature, see, forexample, U.S. Pat. Nos. 3,791,932; 3,839,153; 3,850,752; 3,850,578;3,853,987; 3,867,517; 3,879,262; 3,901,654; 3,935,074; 3,984,533;3,996,345; 4,034,074; 4,098,876; 4,879,219; 5,011,771 and 5,281,521;“Oligonucleotide Synthesis” Gait, M. J., ed. (1984); “Nucleic AcidHybridization” Hames, B. D., and Higgins S. J., eds. (1985);“Transcription and Translation” Hames, B. D., and Higgins S. J., eds.(1984); “Animal Cell Culture” Freshney, R. I., ed. (1986); “ImmobilizedCells and Enzymes” IRL Press, (1986); “A Practical Guide to MolecularCloning” Perbal, B., (1984) and “Methods in Enzymology” Vol. 1-317,Academic Press; “PCR Protocols: A Guide To Methods And Applications”,Academic Press, San Diego, Calif. (1990); Marshak et al., “Strategiesfor Protein Purification and Characterization—A Laboratory CourseManual” CSHL Press (1996); all of which are incorporated by reference asif fully set forth herein. Other general references are providedthroughout this document. The procedures therein are believed to be wellknown in the art and are provided for the convenience of the reader. Allthe information contained therein is incorporated herein by reference.

Example 1 A Host-Proteome Signature for Distinguishing Between Bacterialand Viral Infections: A Prospective Multi-Center Observational Study

Methods

Study population: A total of 1002 patients took part in the study.Pediatric patients (≤18 years) were recruited from pediatric emergencydepartments (PED), pediatric wards and surgical departments, and adults(>18 years) from emergency departments (ED), internal medicinedepartments and surgical departments. Informed consent was obtained fromeach participant or legal guardian, as applicable. Inclusion criteriafor the infectious disease cohort included: clinical suspicion of anacute infectious disease, peak fever >37.5° C. since symptoms onset, andduration of symptoms≤12 days. Inclusion criteria for the control groupincluded: clinical impression of a non-infectious disease (e.g. trauma,stroke and myocardial infarction), or healthy subjects. Exclusioncriteria included: evidence of any episode of acute infectious diseasein the two weeks preceding enrollment; diagnosed congenital immunedeficiency; current treatment with immunosuppressive or immunomodulatorytherapy; active malignancy, proven or suspected human immunodeficiencyvirus (HIV)-1, hepatitis B virus (HBV), or hepatitis C virus (HCV)infection (FIG. 1A). Importantly, in order to enable broadgeneralization, antibiotic treatment at enrollment did not causeexclusion from the study.

Enrollment process and data collection: For each patient, the followingbaseline variables were recorded: demographics, physical examination,medical history (e.g. main complaints, underlying diseases,chronically-administered medications, comorbidities, time of symptomonset, and peak temperature), complete blood count (CBC) obtained atenrollment, and chemistry panel (e.g. creatinine, urea, electrolytes,and liver enzymes). A nasal swab was obtained from each patient forfurther microbiological investigation, and a blood sample was obtainedfor protein screening and validation. Additional samples were obtainedas deemed appropriate by the physician (e.g. urine and stool samples incases of suspected urinary tract infection [UTI], and gastroenteritis[GI] respectively). Radiological tests were obtained at the discretionof the physician (e.g. chest X-ray for suspected lower respiratory tractinfection [LRTI]). Thirty days after enrollment, disease course andresponse to treatment were recorded. All information was recorded in acustom electronic case report form (eCRF).

Microbiological investigation: Patients underwent two multiplex-PCRdiagnostic assays from nasal swab samples: (i) Seeplex® RV15 (n=713),for detection of parainfluenza virus 1, 2, 3, and 4, coronavirus229E/NL63, adenovirus A/B/C/D/E, bocavirus 1/2/3/4, influenza virus Aand B, metapneumovirus, coronavirus OC43, rhinovirus A/B/C, respiratorysyncytial virus A and B, and Enterovirus, and (ii) Seeplex® PB6 (n=633)for detection of Streptococcus pneumoniae, Haemophilus influenzae,Chlamydophila pneumoniae, Legionella pneumophila, Bordetella pertussis,and Mycoplasma pneumoniae. Multiplex-PCR assays were performed by acertified service laboratory. Patients were also tested for additionalpathogens according to their suspected clinical syndrome, including:blood culture (n=420), urine culture (n=188) and stool culture forShigella spp., Campylobacter spp. and Salmonella spp. (n=66);serological testing (IgM and/or IgG) for cytomegalovirus (CMV),Epstein-Barr virus (EBV), Mycoplasma Pneumonia, and Coxiella burnetii(Q-Fever) (n=167, n=130, n=206 and n=41 respectively).

Establishing the reference standard: The Clear Diagnosis, Unanimous andMajority cohorts: A rigorous composite reference standard was createdfollowing recommendations of the Standards for Reporting of DiagnosticAccuracy (STARD).³⁸ First, a thorough clinical and microbiologicalinvestigation was performed for each patient as described above. Then,all the data collected throughout the disease course was reviewed by apanel of three physicians. For adult patients (>18 years) the panelincluded the attending physician and two infectious disease specialists,while for children and adolescents (≤18 years) it included the attendingpediatrician, an infectious disease expert and a senior attendingpediatrician. Each panel member assigned one of the following diagnosticlabels to each patient: (i) bacterial; (ii) viral; (iii) no apparentinfectious disease or healthy (controls); and (iv) indeterminate.Patients with mixed infections (bacteria plus virus) were labeled asbacterial because they are managed similarly (e.g. treated withantibiotics). Importantly, the panel members were blinded to thelabeling of their peers and to the results of the signature.

This process was used to create three cohorts with an increasing levelof diagnostic certainty (FIG. 1A):

-   (i) Majority cohort: Patients were assigned the same label by at    least two of the three panel members;-   (ii) Unanimous cohort (a subgroup of the Majority cohort): Patients    were assigned the same label by all three panel members (the terms    “unanimous cohort” and “consensus cohort” are used herein    interchangeably); and-   (iii) Clear Diagnosis cohort (a subgroup of the Unanimous cohort):    Bacterial labeled patients were unanimously diagnosed by all three    panel members, had WBC >15,000/μl (a cutoff indicative of increased    bacterial infection risk¹¹) and one of the following microbiological    confirmations: bacteremia (with positive blood culture), bacterial    meningitis (with positive CSF culture), pyelonephritis (with    positive urine culture and ultrasound demonstration of renal    involvement), UTI (with positive urine culture), septic shock (with    positive blood culture), or peritonsillar abscess (proven by    surgical exploration or computerized tomography). Viral labeled    patients were unanimously diagnosed by panel members and had and a    positive test result of a virus.

Additionally, control labeled patients were unanimously diagnosed by allthree panel members.

Samples, procedures and protein measurements: Venous blood samples werestored at 4° C. for up to 5 hours on site and subsequently fractionatedinto plasma, serum and total leukocytes and stored at −80° C. Nasalswabs and stool samples were stored at 4° C. for up to 72 hours andsubsequently transported to a certified service laboratory for multiplexPCR-based assay. In the screening phase, host-proteins were measured inserum and leukocytes using enzyme linked immunosorbent assay (ELISA),Luminex technology, protein arrays and Flow cytometry (on freshlyisolated leukocytes). After screening and signature construction (seeHost-proteome screening section), three proteins were selected andmeasured as follows: CRP was measured via either Cobas 6000, CobasIntegra 400, Cobas Integra 800, or Modular Analytics P800 (Roche). TRAILand IP-10 were measured using commercial ELISA kits (MeMed Diagnostics).

Statistical analysis: The primary analysis was based on area under thereceiver operating characteristics curve (AUC), Sensitivity (TP/P),Specificity (TN/N), Positive likelihood ratio(LR+=Sensitivity/[1−Specificity]), Negative likelihood ratio(LR−=[1−Sensitivity]/Specificity) and Diagnostic odds ratio(DOR=LR+/LR−), where P, N, TP and TN correspond to positives (bacterialpatients), negatives (viral patients), true positives (correctlydiagnosed bacterial patients), and true negatives (correctly diagnosedviral patients), respectively. Statistical analysis was performed withMATLAB. Sample size calculations are presented in Example 2 hereinbelow.

Host-proteome screening: A general overview of the process fordeveloping, training and testing the multivariate logistic model isdepicted in FIG. 1B. Briefly, a systematic literature screen andbioinformatics analysis was performed that identified 600 proteincandidates that were likely to be differentially expressed in peripheralblood samples of bacterial versus viral patients, some of which have aknown role in the host immune response to infection and others with nodirect link to the immune system. Next, each protein candidate wasmeasured on 20-30 patients from the training set (50% viral and 50%bacterial) and a Wilcoxon rank-sum (WS) P-value <0.01 was used to screenproteins with statistically significant differential measurements. Thisresulted in a set of 86 proteins (false discovery rate [FDR] of 0.07).Each of these proteins was then evaluated in 100 additional patients(50% viral and 50% bacterial) and further screened using a t-test cutoffof P<10⁻⁴, resulting in 14 proteins that were significantlydifferentially expressed in viral versus bacterial patients (FDR<0.001).

Signature development and validation: A feature selection process wasapplied to identify the optimal combination of proteins. Two featureselection schemes were used: mutual-information min-max³⁹ and forwardgreedy wrapper⁴⁰, which use a series of iterations to add or removefeatures. The process was terminated when the increase in performance onthe training set was no longer statistically significant (P>0.05). Bothprocesses converged to the same final set of three proteins. Tointegrate the protein levels into a single score, multiple computationalmodels were examined. Their performances were not significantlydifferent (P>0.1 as further detailed in Example 2 herein below). AMultinomial Logistic Regression (MLR) model was chosen because providesa probabilistic interpretation by assigning a likelihood score to apatient's diagnosis. The signature uses this property to filter outpatients whose probability of bacterial infection is intermediate:between 0.35 and 0.55. The term ‘marginal immune response’ is used todescribe these patients because their profile borders between bacterialand viral host-responses. The patients in the Majority cohort weredivided into training and test sets, each comprising 50% of the patients(FIG. 1B). The training set included the 120 patients who participatedin the screening process and additional patients that were randomlyassigned. The test set included the remaining patients and was used forindependent assessment of the signature performance. Importantly, noneof the test set patients were used to train the algorithms, or to selectthe proteins. A leave-10%-out cross-validation was used to estimatemodel performance. More details on the model construction are providedin Example 2 herein below).

Results

Patient characteristics: Three physicians independently assigned a labelto each patient (either bacterial, viral, controls, or indeterminate).The labels were used to create three cohorts with increasing level ofdiagnostic certainty: Majority (n=765), Unanimous (n=639) and ClearDiagnosis (n=312) cohorts (FIG. 1A). Additionally, 98 patients werelabeled as indeterminate, because the physicians could not establishdisease etiology or there was no majority labeling. A detailedcharacterization of the Majority cohort is depicted in Table 1. Briefly,the cohort was balanced with respect to gender (47% females, 53% males)and included 56% pediatric patients (≤18 years) and 44% adults (>18years). Patients presented with a wide range of clinical syndromes (e.g.RTI, UTI, and systemic infections), maximal temperatures (36-41.5° C.),time from symptoms onset (0-12 days), comorbidities, and medications(Table 1 and FIGS. 6A-12B). Altogether, 56 pathogen species weredetected that are responsible for the vast majority of acute infectiousdiseases in the Western world (FIGS. 7A-7B).

TABLE 1 Children Adults Total (≤18 years) (>18 years) Criteria n = 765 n= 432 n = 333 Age (years) <3 211 (28) 3-6 93 (12) 6-9 46 (6) 9-18 82(11) 18-30 55 (7) 30-60 161 (21) >60 117 (15) Gender Female 363 (47) 205(47) 158 (47) Maximal Temperature (° C.) <37.5 106 (14) 28 (6) 78 (23)37.5-38.4 154 (20) 68 (16) 86 (26) 38.5-39.4 294 (38) 164 (38) 130 (39)39.5-40.4 196 (26) 157 (36) 39 (12) >40.5 15 (2) 15 (3) 0 (0) Time fromsymptoms onset (days) 0-1 175 (24) 118 (27) 57 (17) 2-3 265 (36) 161(37) 104 (31) 4-5 161 (22) 89 (21) 72 (22) 6-7 109 (15) 52 (12) 57 (17)8-9 10 (1) 2 (0.5) 8 (2) 10-12 14 (2) 2 (0.5) 12 (4) N/A 31 (4) 8 (2) 23(7) Clinical syndrome Cellulitis 28 (4) 7 (2) 21 (6) CNS 14 (2) 9 (2) 5(2) GI 89 (11.5) 66 (15) 23 (7) LRTI 158 (21) 84 (19) 74 (22)Non-infectious 112 (14.5) 29 (7) 83 (25) Other 12 (1.5) 4 (1) 8 (2.5)Systemic 150 (19.5) 110 (26) 40 (12) URTI 145 (19) 104 (24) 41 (12) DTI57 (7) 19 (4) 38 (11) Recruiting site Pediatrics & Internal 293 (38) 137(32) 156 (47) PED & ED 472 (62) 295 (68) 177 (53) Hospitalizationduration (days) Not hospitalized 272 (36) 174 (40) 98 (29) 1-2 206 (28)126 (29) 80 (24) 3-4 170 (22) 94 (22) 76 (23) 5-6 53 (7) 24 (6) 29 (9)7-8 31 (4) 7 (1.5) 24 (7) >8 33 (4) 7 (1.5) 26 (8) Season Autumn 181(24) 111 (26) 70 (21) Spring 208 (27) 124 (29) 84 (25) Summer 170 (22)98 (23) 72 (22) Winter 206 (27) 99 (23) 107 (32) Smoking Yes 74 (10) 0(0) 74 (22) No 691 (90) 432 (100) 259 (78) Antibiotic prescription Yes432 (56) 207 (48) 225 (68) No 333 (44) 225 (52) 108 (32) Detectedmicroorganisms Not detected 219 (29) 79 (18) 140 (42) Viruses AdenovirusA/B/C/D/E 50 (7) 47 (11) 3 (1) Bocavirus 1/2/3/4 9 (1) 9 (2) 0 (0) CMV &EBV 25 (3) 23 (5) 2 (0.6) Coronavirus 19 (2) 14 (3) 5 (2) 229E/NL63/OC43Enteric viruses 19 (2) 16 (4) 3 (1) Enterovirus 21 (3) 20 (5) 1 (0.3)Influenza A virus 45 (6) 24 (6) 21 (6) Influenza B virus 19 (2) 14 (3) 5(2) Metapneumovirus 17 (2) 13 (3) 4 (1) Parainfluenza 1/2/3/4 48 (6) 41(9) 7 (2) Respiratory 40 (5) 38 (9) 2 (0.6) syncytial virus A/BRhinovirus A/B/C 87 (11) 73 (17) 14 (4) Bacteria Atypical bacteria 27(4) 7 (2) 20 (6) E. coli 44 (6) 17 (4) 27 (8) Enterococcus faecalis 10(1) 0 (0) 10 (3) Group A Strep 19 (2) 16 (4) 3 (1) Haemophilusinfluenzae 179 (23) 148 (34) 31 (9) Streptococcus pneumoniae 306 (40)207 (48) 99 (30)

Table 1—Baseline characteristics of the majority cohort patients. Valuesare numbers (percentages). Only microorganisms that were detected inmore than 5 patients are presented. CNS—central nervous system,GI—gastroenteritis, LRTI—lower respiratory tract infection, UTRI—upperrespiratory tract infection, UTI—urinary tract infection, N/A—healthycontrols or patients in which data was not obtained. Influenza Asubgroup included H1N1 strains. The atypical bacteria subgroup includedChlamydophila pneumoniae, Mycoplasma pneumonia and Legionellapneumophila. The Enteric viruses subgroup included Rota virus,Astrovirus, Enteric Adenovirus and Norovirus G I/II. In the clinicalsyndrome analysis the LRTI group included pneumonia, bronchiolitis,acute bronchitis, and laryngitis; URTI group included pharyngitis, acuteotitis media, acute sinusitis and acute tonsillitis.

Signature performance on the Clear Diagnosis, Unanimous and Majoritycohorts: Of the 600 screened host-proteins and their combinations, thebest signature for discriminating bacterial, viral and control patientsin the Majority cohort training set included three soluble proteins:TNF-related apoptosis-inducing ligand (TRAIL), Interferon gamma-inducedprotein 10 (IP-10), and C-reactive protein (CRP) (FIGS. 2A-2C).Signature AUC for distinguishing between bacterial and viral infectionson the test set of the Majority cohort was 0.94±0.04. Similar resultswere obtained using leave-10%-out cross-validation on the entireMajority cohort (AUC=0.94±0.02). The signature significantlyoutperformed all the individual proteins evaluated in the screeningphase (P<10⁻⁶). The training and testing procedures were repeated on theUnanimous and Clear Diagnosis cohorts, yielding AUCs of 0.96±0.02 and0.99±0.01, respectively. This stepwise increase in performance isaligned with the increased certainty of reference standard assignment inthe three cohorts (Table 2, herein below).

TABLE 2 Signature measures of accuracy for diagnosing bacterial vs.viral infections B. Marginal immune response filter A. All patientsClear Clear Majority Unanimous diagnosis Majority Unanimous diagnosisAccuracy cohort cohort cohort cohort cohort cohort measure 0.94 0.970.99 0.94 0.96 0.99 AUC (0.92, 0.96) (0.95, 0.99) (0.98, 1.00) (0.92,0.96) (0.94, 0.98) (0.98, 1.00) 0.91 0.93 0.96 0.88 0.90 0.94 Total(0.88, 0.94) (0.9, 0.96) (0.93, 0.99) (0.85, 0.90) (0.87, 0.92) (0.91,0.97) accuracy 0.92 0.94 0.96 0.87 0.88 0.96 Sensitivity (0.88, 0.96)(0.9, 0.98) (0.88, 1.00) (0.83, 0.91) (0.84, 0.91) (0.88, 1.00) 0.890.93 0.97 0.90 0.92 0.93 Specificity (0.86, 0.89) (0.9, 0.96) (0.89,0.97) (0.86, 0.93) (0.89, 0.96) (0.89, 0.97) 8.4 13.4 32.0 8.7 11.0 13.7LR+ (6, 12) (8, 21) (13, 78) (6, 12) (7, 16) (8, 24) 0.09 0.07 0.04 0.140.13 0.04 LR− (0.06, 0.13) (0.04, 0.11) (0.01, 0.26) (0.11, 0.19) (0.09,0.18) (0.01, 0.27) 93 208 776 60 84 319 DOR (53, 164) (99, 436) (92,6528) (37, 98) (47, 150) (43, 2383) A. Performance estimates and their95% CIs were obtained using a leave-10%- out cross-validation on allpatients in the Clear Diagnosis cohort (n_(Bacterial) = 27, n_(Viral) =173), Unanimous (n_(Bacterial) = 256, n_(Viral) = 271), and Majority(n_(Bacterial) = 319, n_(Viral) = 334) cohorts. B. The analysis wasrepeated after filtering out patients with a marginal immune response(Clear Diagnosis [n_(Bacterial) = 27, n_(Viral) = 159, n_(marginal) =14], Unanimous [n_(Bacterial) = 233, n_(Viral) = 232, n_(marginal) =62], and Majority [n_(Bacterial) = 290, n_(Viral) = 277, n_(marginal) =88]), which resembles the way clinicians are likely to use thesignature.

A. Performance estimates and their 95% CIs were obtained using aleave-10%-out cross-validation on all patients in the Clear Diagnosiscohort (n_(Bacterial)=27, n_(Viral)=173), Unanimous (u_(Bacterial)=256,u_(Viral)=271), and Majority (n_(Bacterial)=319, n_(Viral)=334) cohorts.B. The analysis was repeated after filtering out patients with amarginal immune response (Clear Diagnosis [n_(Bacterial)=27,n_(Viral)=159, n_(Marginal)=14], Unanimous [n_(Bacterial)=233,n_(Viral)=232, n_(marginal)=62], and Majority [n_(Bacterial)=290,n_(Viral)=277, n_(marginal)=88]), which resembles the way clinicians arelikely to use the signature.

Next, the present inventors used the signature to distinguish betweeninfectious (bacterial or viral) and non-infectious controls on theMajority cohort test set, yielding an AUC of 0.96±0.02. Furtherevaluation using leave-10%-out cross-validation gave similar results(AUC=0.96±0.01). The signature outperformed any of the individualproteins (P<10⁻⁸). Again, evaluation on the Unanimous and ClearDiagnosis cohorts showed improved AUCs of 0.97±0.02, and 0.97±0.03,respectively. To obtain conservative estimations of signatureperformance, the analysis that follows focuses on the Majority cohort.

Comparison with laboratory measurements, clinical parameters, andwell-established biomarkers: The signature was compared withwell-established clinical parameters and laboratory measurements,including white blood count (WBC), absolute neutrophil count (ANC),percentage neutrophils, maximal temperature, pulse, and respiratory rate(FIG. 3A and Example 2). The signature surpassed all individualparameters (P<10⁻¹⁸). Next, the signature was compared to a combinationof several clinical parameters. To this end, multinomial logistic modelswere generated for all combinations of up to four clinical parameters.The best performing pair, triplet and quadruplet are depicted in FIG. 3A(adding a fifth parameter did not improve performance). The signaturewas significantly better than the best performing clinical parameterscombination (P<10⁻¹⁵), which consisted of ANC, pulse, % lymphocytes and% monocytes, (AUC=0.94±0.02 vs. 0.77±0.04). Next, the signatureperformance was compared to PCT and CRP, two proteins routinely used inclinical practice to diagnose sepsis and bacterial infections (Example2). The signature performed significantly better than both proteins(P<10⁻⁸ and P<10⁻⁶, respectively). The signature also performed betterthan a wide range of host-proteins with an established role in theimmune response to infection, including sepsis and bacterial-related(e.g. TREM, IL-6 and IL-8), virus-related (e.g. IFN-γ and IL-2), andinflammation-related (e.g. IL-1a and TNF-α) proteins (P<10⁻⁸) (FIG. 3Band Example 2, herein below).

Signature performance is robust across different patient subgroups:Patient and pathogen heterogeneity, which are inherent in real-lifeclinical settings, might negatively affect the diagnostic utility of anyindividual host-biomarker. To examine whether the signature, acombination of multiple biomarkers, can maintain steady performancedespite patient-to-patient variability, subgroup analyses wereperformed. The signature was robust (AUCs between 0.87 and 1.0) across awide range of patient characteristics, including age, clinical syndrome,time from symptom onset, maximal temperature, pathogen species,comorbidities, treatment with medications for chronic diseases, andclinical site (FIG. 4 and Example 2, herein below). The signature wasalso tested on the subgroup of patients who were technically excluded,but had unanimous labeling by the expert panel, which yielded an AUC of0.96±0.06 (n_(Bacterial)=27, n_(Viral)=14). This might suggest that thesignature is applicable more broadly to conditions that were initiallyexcluded (e.g. sub-febrile patients).

Signature performance remains unaffected by the presence of potentialcolonizers: Many disease-causing bacteria are also part of the naturalflora, and are frequently found in asymptomatic subjects.^(12,42-44)Such bacteria pose a considerable diagnostic challenge, because merelydetecting them does not necessarily imply a causative role in thedisease; therefore, appropriate treatment may remain unclear. Thepresent inventors asked whether the signature performance is affected bytheir presence.

Streptococcus pneumoniae (SP) and Haemophilus influenzae (HI), detectedby PCR on nasal swabs, were the two most common bacteria in the Majoritygroup (Table 1, herein above). High rates of SP and HI were foundamongst both bacterial and viral patients (SP: 36% and 47%; HI: 20% and32%), substantiating the understanding that their mere presence does notnecessarily cause a disease.¹² The patients were stratified based onwhether or not they had SP (SP+: n_(Bacterial)=116, n_(Viral)=157; SP−:n_(Bacterial)=203, n_(Viral)=177) and AUC performance of the two groupswas compared. A significant difference was not observed (0.93±0.03 vs.0.94±0.02, P=0.31). The presence or absence of HI did not affectsignature performance either (0.94±0.04 vs. 0.93±0.02; HI+:n_(Bacterial)=63, n_(Viral)=106; HI−: n_(Bacterial)=256, n_(Viral)=228,P=0.34). This indicates that the signature remains unaffected bycarriage of SP and HI.

Discussion

A rigorous composite reference standard strategy was constructed thatincluded the collection of clinical data, a chemistry panel, and a widearray of microbiological tests, followed by labeling by threeindependent physicians. This process generated a hierarchy of threesub-cohorts with decreasing size and increasing reference standardcertainty: Majority, Unanimous and Clear Diagnosis. The respectivesignature AUCs were 0.94±0.02, 0.96±0.02, and 0.99±0.01. This stepwiseincrease in performance may be attributed to the increase in referencestandard certainty. However, the increased accuracy, particularly in theClear Diagnosis cohort, may also be partially due to a selection bias ofpatients with severe illness or straightforward diagnosis. Therefore,the primary analysis presented herein focused on the Majority cohort,which captures a wider spectrum of illness severity anddifficult-to-diagnose cases. This cohort potentially includes someerroneous labeling, thereby leading to conservative estimations of thesignature accuracy.

The signature addresses several challenges of current microbiologicaltests. (i) The difficulty of diagnosing inaccessible or unknowninfection sites. The signature accurately diagnosed such cases,including lower respiratory tract infections (AUC 0.95±0.03, n=153) andfever without source (AUC=0.97±0.03, n=123). (ii) Prolonged time toresults (hours to days). The signature measures soluble proteins, whichare readily amenable to rapid measurement (within minutes) onhospital-deployed automated immunoassay machines and point-of-caredevices. (iii) Mixed infections may lead to diagnostic uncertainty,because detection of a virus does not preclude bacterialco-infection.^(14,15) The signature addresses this by classifying mixedinfections together with pure bacterial infections, thus promptingphysicians to manage both groups similarly with regard to antibioticstreatment. The fact that mixed co-infections elicited a proteomehost-response that is similar to pure bacterial, rather than a mixtureof responses, may indicate pathway dominance of bacterial over viral.(iv) A significant drawback of microbiological tests, PCRs inparticular, is detection of potential colonizers in subjects withnon-bacterial diseases.^(12,13) The signature performance was unaffectedby the presence or absence of potential colonizers.

Host-proteins, such as PCT, CRP and IL-6, are routinely used to assistin the diagnosis of bacterial infections because they convey additionalinformation over clinical symptoms, blood counts and microbiology.¹¹However, inter-patient and pathogen variability limit theirusefullness.²¹⁻²⁷ Combinations of host-proteins have the potential toovercome this, but have thus far yielded insignificant-to-moderatediagnostic improvement over individual proteins.^(11,35-37) This modestimprovement may be due to the reliance on combinations ofbacterial-induced proteins that are sensitive to the same factors, andare therefore less capable of compensating for one another. Accordingly,a larger improvement was observed in combinations that includedhost-proteins, clinical parameters and other tests.^(11,35-37) Obtainingthese multiple parameters in real-time, however, is often not feasible.

To address this, a combination of proteins with complementary behaviorswas identified. Specifically, it was found that TRAIL was induced inresponse to viruses and suppressed by bacteria, IP-10 was higher inviral than bacterial infections, and CRP was higher in bacterial thanviral infections. While the utility of elevated CRP to suggest bacterialinfections is well established^(31,45), the inclusion of novelviral-induced proteins, to complement routinely used bacterial-inducedproteins, substantially contributed to the signature's robustness acrossa wide range of subgroups, including time from symptom onset, pathogenspecies and comorbidities among others. For example, adenoviruses, animportant subgroup of viruses that cause 5%-15% of acute infections inchildren are particularly challenging to diagnose because they induceclinical symptoms that mimic a bacterial infection.⁴⁶ Routine laboratoryparameters perform poorly on this subgroup compared to the signature(AUCs=0.60±0.10 [WBC], 0.58±0.10 [ANC], 0.88±0.05 [signature]; n=223).

Despite advances in infectious disease diagnosis, timely identificationof bacterial infections remains challenging, leading to antibioticmisuse with its profound health and economic consequences. To addressthe need for better treatment guidance, the present inventors havedeveloped and validated a signature that combines novel and traditionalhost-proteins for differentiating between bacterial and viralinfections. The present finding in a large sample size of patients ispromising, suggesting that this host-signature has the potential to helpclinicians manage patients with acute infectious disease and reduceantibiotic misuse.

Example 2 A Host-Proteome Signature for Distinguishing Between Bacterialand Viral Infections: A Prospective Multi-Center ObservationalStudy—Supplementary Material

Measures of accuracy: The signature integrates the levels of threeprotein biomarkers measured in a subject, and computes a numerical scorethat reflects the probability of a bacterial vs. viral infection. Toquantify the diagnostic accuracy of the signature a cutoff on the scorewas used and the following measures were applied: Sensitivity,specificity, positive predictive value (PPV), negative predictive value(NPV), total accuracy, positive likelihood ratio (LR+), negativelikelihood ratio (LR−), and diagnostic odds ratio (DOR). These measuresare defined as follows:

${Sensitivity} = \frac{TP}{{TP} + {FN}}$${Specificity} = \frac{TP}{{TP} + {FN}}$${{total}{accuracy}} = \frac{{TP} + {TN}}{{TP} + {FN} + {TN} + {FP}}$${PPV} = {\frac{TP}{{TP} + {FN}} = \frac{{sensitivity} \cdot {prevalence}}{{{sensitivity} \cdot {prevalence}} + {\left( {1 - {specificity}} \right) \cdot \left( {1 - {prevalence}} \right)}}}$${NPV} = {\frac{TN}{{TN} + {FN}} = \frac{{specificity} \cdot \left( {1 - {prevalence}} \right)}{{{specificity} \cdot \left( {1 - {prevalence}} \right)} + {\left( {1 - {sensitivity}} \right) \cdot ({prevalence})}}}$${LR}+=\frac{Sensitivity}{1 - {Specificity}}$${LR}+=\frac{1 - {Sensitivity}}{Specificity}$${DOR} = \frac{{LR} +}{{LR} -}$

P, N, TP, FP, TN, FN are positives, negatives, true-positives,false-positives, true-negatives, and false-negatives, respectively.Prevalence is the relative frequency of the positive class (i.e.,prevalence=P/(P+N)). Unless mentioned otherwise, positives and negativesrefer to patients with bacterial and viral infections, respectively.

The area under the receiver operating curve (AUC) was also used toperform cutoff independent comparisons of different diagnostic methods.For details on formulation and confidence interval (CI) computation ofthe AUC see Hanley and McNeil.¹ 95% CIs of the accuracy measuresthroughout this document are reported.

Sample size: The primary study objective was to obtain the performanceof the signature for classifying patients with viral and bacterialetiologies. It was estimated that the sample size required to reject thenull hypothesis that the sensitivity and specificity over the entirepopulation, P, are lower than P0=75% with significance level of 1%,power of 90% for a difference of 15% (P1−P0≥15%), which yielded 394patients (197 viral and 197 bacterial). Additionally it was anticipatedthat roughly 15% of the patients will have an indeterminate source ofinfection, 10% would be excluded for technical reasons and 10% will behealthy or non-infectious controls. Taken together, the study requiredthe recruitment of at least 607 patients. This requirement was fulfilledbecause 1002 patients were recruited.

Constructing a computation model logistic model: To integrate theprotein levels into a single predictive score, multiple computationalmodels were examined including Artificial Neural Networks (ANN), SupportVector Machines (SVM), Bayesian Networks (BN), K-Nearest Neighbor (KNN)and Multinomial Logistic Regression (MLR).^(2,3) The AUCs fordistinguishing between bacterial and viral infections obtained on theMajority cohort using a leave-10%-out cross validation were 0.93±0.02(ANN), 0.93±0.02 (SVM [linear]), 0.94±0.02 [SVM (radial basisfunction)], 0.92±0.02 (BN), 0.91±0.02 (KNN) and 0.94±0.02 (MLR).Significant difference in the performances of ANN, SVM and MLR models(P>0.1 when comparing their AUCs) were not observed. The presentinventors chose to use MLR because it provides a probabilisticinterpretation by assigning a likelihood score to a patient's diagnosis.

The present inventors trained and tested the MLR signature fordistinguishing between bacterial and non-bacterial etiologies. Since theprevalence of underlying etiologies varies across different clinicalsettings, the model priors were adjusted to reflect equal baselineprevalence (50% bacterial and 50% non-bacterial). Within thenon-bacterial group the priors were adjusted to 45% viral and 5%non-infectious, to reflect the anticipated higher prevalence of viralversus non-infectious patients among subjects with suspicious for acuteinfection. The MLR weights and their respective 95% confidenceintervals, as well as the p-values associated with each coefficient aresummarized in Tables 3-4 herein below. In the bacterial versus viralinfection analysis the probabilities were adjusted to sum up to 1(P_(b_adjusted)=[P_(b)+P_(v)] and P_(b_adjusted)=[P_(b)+P_(v)], whereP_(b) and P_(v) correspond to the probability of bacterial and viralinfections respectively).

TABLE 3 MLR coefficients and their respective standard error SecondCoordinate First Coordinate δ₁ (bacterial) δ₀ (viral) b₀ = −0.378 ±0.732 a₀ = −1.299 ± 0.651 Constant b₁ = −0.020 ± 0.0084 a₁ = 0.0088 ±0.0064 TRAIL b₂ = 0.0875 ± 0.015 a₂ = 0.0605 ± 0.0145 CRP b₃ = 0.0050 ±0.0014 a₃ = 0.0053 ± 0.0014 IP-10

TABLE 4 The p-values associated with each MLR coefficient. Class(bacterial) Class (viral) <0.001 <0.001 Constant <0.001 0.008 TRAIL<0.001 <0.001 CRP <0.001 <0.001 IP-10

Logistic calibration curves: In order to assess the validity of the MLRmodel, the calculated prediction probabilities were compared with theactually observed outcomes (FIG. 5 ). The predicted probabilities arehighly compatible with the observed ones, further demonstrating themodel validity.

Summary of the patient cohorts used in this study: A total of 1002patients were recruited and 892 were enrolled (110 were excluded basedon pre-determined exclusion criteria). Based on the reference standardprocess described in the ‘Methods’ section of Example 1, patients wereassigned to four different diagnosis groups: (i) bacterial; (ii) viral;(iii) no apparent infectious disease or healthy (controls); and (iv)indeterminate. Patients with mixed infections (bacteria plus virus) werelabeled as bacterial because they are managed similarly (e.g. treatedwith antibiotics) (FIG. 1A). In total, 89% of all enrolled patients wereassigned a diagnosis, a rate which approaches the literature-documentedlimit. The following sections provide a detailed description of patientcharacteristics, which includes all the patients with a final diagnosis(n=794): 765 patients of the Majority cohort and 29 patients for whichthe serum samples were depleted during the screening phase (FIGS.1A-1B).

Age and gender distribution: Patients of all ages were recruited to thestudy. The patients with agreed diagnosis (diagnosed patients; n=794)included more pediatric (≤18 years) than adult (>18 years) patients (445patients [56%] vs. 349 [44%]). The age distribution was relativelyuniform for patients aged 20-80 years and peaked at <4 years of age forpediatric patients (FIGS. 6A-6B). The observed age distribution forpediatric patients is consistent with that expected and represents thebackground distribution in the inpatient setting⁷ (e.g., the emergencydepartment [ED], pediatrics departments, and internal departments).Patients of both genders were recruited to the study. The patientpopulation was balanced in respect to gender distribution (47% females,53% males).

Detected pathogens: A wide panel of microbiological tools were used inorder to maximize pathogen detection rate. At least one pathogen wasdetected in 65% of patients with an acute infectious disease (56% of all794 diagnosed patients). A total of 36 different pathogens were activelydetected using multiplex PCR, antigen detection, and serologicalinvestigation. Additional 20 pathogens were detected using standardculture techniques or in-house PCR. Altogether, 56 different pathogensfrom all major pathogenic subgroups were detected (FIG. 7A). This rateof pathogen identification is similar to that reported in previouslypublished studies and included pathogens from all major pathogenicsubgroups (Gram-negative bacteria, Gram-positive bacteria, atypicalbacteria, RNA viruses, and DNA viruses). In 13% of the patients,pathogens from more than one of the aforementioned pathogenic subgroupswere detected (FIG. 7A).

The pathogenic strains found in this study are responsible for the vastmajority of acute infectious diseases in the Western world and includedkey pathogens such as influenza A/B, respiratory syncytial virus (RSV),parainfluenza, E. coli, Group A Streptococcus, etc. Notably, analysis ofthe detected pathogens revealed that none of the pathogens is dominant(FIG. 7B).

Involved physiologic systems and clinical syndromes: The infectiousdisease patients (all diagnosed patients [n=794], excluding those withnon-infectious diseases or healthy subjects, n=673) presented withinfections in a variety of physiologic systems (FIG. 8 ). The mostfrequently involved physiologic system was the respiratory system (46%),followed by systemic infections (22%). All infections that did notinvolve the aforementioned systems and were not gastrointestinal,urinary, cardiovascular, or central nervous system (CNS) infections werecategorized as ‘Other’ (e.g., cellulitis, abscess). The observeddistribution of physiologic system involvement represents the naturaldistribution and is consistent with that reported for large cohorts ofpatients sampled year-round.

The diagnosed patients in the present study (n=794) presented with avariety of clinical syndromes (FIGS. 9A-9B) that reflects the expectedclinical heterogeneity in a cohort of pediatric and adult patientscollected year-round. The most frequent clinical syndrome was LRTI (21%)including mainly pneumonia, bronchitis, bronchiolitis, chronicobstructive pulmonary disease (COPD) exacerbation, and non-specificLRTI. The second most frequent syndrome was systemic infection (19%)including mainly fever without a source and occult bacteremia cases.Systemic infections were primarily detected in children <3 years of agebut were also detected in a few adult patients. Systemic infectionsconstitute a real clinical challenge as balancing between patient riskand the costs of testing/treatment is unclear. The third most frequentclinical syndrome was URTI (19%) including mainly acute tonsillitis,acute pharyngitis, non-specific URTI, acute sinusitis, and acute otitismedia. The next most frequent syndromes were gastroenteritis (12%), UTI(7%), and cellulitis (4%). CNS infections (2%) included septic andaseptic meningitis. Additional clinical syndromes (1%) were classifiedas ‘Other’ and included less common infections (e.g., otitis externa,epididymitis, etc.). The observed pattern of clinical syndromedistribution represents most of the frequent and clinically relevantsyndromes and is consistent with previously published large studies.

Core body temperature: Core body temperature is an important parameterin evaluating infectious disease severity. The distribution of maximalbody temperatures was examined in all of the diagnosed patients (n=794)using the highest measured body temperature (per-os or per-rectum). Thedistribution of the maximal body temperatures was relatively uniformbetween 38° C. and 40° C. with a peak of at 39° C. (FIG. 10 ). Bodytemperature <37.5° C. was reported for 15% of patients (the subgroup ofpatients with non-infectious diseases or healthy subjects). Bodytemperature ≥40.5° C. was rare (<3% of patients). Altogether, theobserved distribution represents the normal range of temperatures in theclinical setting.

Time from symptoms onset: ‘Time from symptoms’ was defined as theduration (days) from the appearance of the first presenting symptom (thefirst presenting symptom could be fever but could also be anothersymptom such as nausea or headache preceding the fever). Thedistribution of ‘time from symptoms’ in our cohort (all diagnosedpatients, n=794) peaked at 2-4 days after the initiation of symptoms(35% of patients) with substantial proportions of patients turning tomedical assistance either sooner or later (FIG. 11 ).

Comorbidities and chronic drug regimens: Comorbidities and chronic drugregimens may, theoretically, affect a diagnostic test. Out of thediagnosed patients 62% had no comorbidities whereas 38% had ≥1 chronicdisease. In addition, 75% of patients were not treated with chronicmedications and 25% were treated with ≥1 chronic medication. The mostfrequent chronic diseases in our patient population were hypertension,hyperlipidemia, lung diseases (e.g., COPD, asthma, etc.), diabetesmellitus (mostly type 2), and ischemic heart disease, mirroring the mostcommon chronic diseases in the Western world (FIG. 12A). Thedistribution of chronic drugs used by our patient population stronglycorrelated with the range of reported chronic diseases (e.g., 29% of thepatients with comorbidities had hyperlipidemia and lipid lowering agentswere the most frequently used drugs). Other frequently used drugsincluded aspirin, blood glucose control drugs, and beta blockers (FIG.12B).

Patient recruitment sites: Pediatric patients (≤18 years) were recruitedfrom pediatric emergency departments (PED), pediatric wards and surgicaldepartments, and adults (>18 years) from emergency departments (ED),internal medicine departments and surgical departments. The pediatric EDwas the most common recruitment site (39%) and the other sites werecomparable (17-20%) reflecting a relatively balanced recruitmentprocess. The ratio between ED patients and hospitalized patients was˜1:1 for adults and ˜2:1 for children (FIG. 13 ).

Characteristics of excluded patients: Of the 1002 patients recruited forthe study, 110 patients (11%) were excluded (some patients fulfilledmore than one exclusion criterion). The most frequent reason forexclusion was having a fever below the study threshold of 37.5° C.(n=54), followed by time from symptom initiation of >12 days (n=26) andhaving a recent (in the preceding 14 days) infectious disease (n=22).Other reasons for exclusion included having an active malignancy (n=14),and being immunocompromised (e.g., due to treatment with animmunosuppressive drug; n=2).

Characteristics of indeterminate patients: A total of 98 patients weredefined as indeterminate based on the inability of the expert panel toreliably establish a composite reference standard, despite the rigorouscollection of laboratory and clinical information. While it is notpossible to directly examine the signature performance in these patientsin the absence of a reference standard, it is possible to analyze theirhost-protein response in order to assess whether they differ frompatients with a reference standard. We compared the distribution ofTRAIL, IP-10 and CRP in acute infection patients with a referencestandard (n=653) to those without a reference standard (n=98). Nostatistically significant difference was observed (Kolmogorov Smirnovtest P=0.20, 0.25, 0.46 for TRAIL, IP-10 and CRP, respectively). Thesimilarity in the host-protein response between patients with andwithout a reference standard implies that the present approach may beuseful for diagnosing indeterminate patients in the clinical setting.

The signature performance remains robust across different patientsubgroups: In Example 1, the present inventors demonstrated that thesignature remained robust across a wide range of patient characteristicsincluding age, clinical syndrome, time from symptom onset, maximaltemperature, pathogen species, comorbidities, and the clinical site withAUCs ranging from 0.87 to 1.0 (FIG. 4 ). In this Example, a review ofthe performance of the signature across additional patient subgroups isprovided.

Stratification by chronic drug regimens: In real-world clinicalpractice, patients are often under various chronic drug regimens, whichcould, potentially, affect the level of proteins comprising thesignature. The present inventors therefore examined whether the mostused drugs (by categories) in our cohort impact the signature'sperformance. None of the evaluated drug groups were associated withsignificant alterations in the signature's accuracy (Table 5).

TABLE 5 Evaluation of the signature's sensitivity to various types ofchronic drug regimens. Viral Bacterial Total AUC Drug patients, npatients, n patients, n [95% CI] category 7 43 50 [0.90, 1.00] 0.95 AntiHypertensive 6 48 54 [0.96, 1.00] 0.99 Anti platelets 7 35 42 [0.80,1.00] 0.90 Anti-acid 4 25 29 [0.93, 1.00] 0.98 Antidepressants 5 35 40[0.88, 1.00] 0.95 Beta Blocker 5 34 39 [0.86, 1.00] 0.94 Ca ChannelBlocker 11 53 64 [0.89, 1.00] 0.94 Cholesterol/TG Lowering 5 35 40[0.74, 1.00] 0.87 Diabetic 5 25 30 [0.83, 1.00] 0.93 Diuretics 4 14 18[0.93, 1.00] 0.98 Hormonal 8 18 26 [0.87, 0.99] 0.95 Inhaled CS 4 21 25[0.84, 1.00] 0.94 Prostate Hypertrophy

Sepsis based stratification: Sepsis is a potentially fatal medicalcondition characterized by a whole-body inflammatory state (calledsystemic inflammatory response syndrome [SIRS]) and the presence of aknown or suspected infection. Patients with a bacterial sepsis benefitfrom early antibiotic therapy; delayed or misdiagnosis can have seriousor even fatal consequences. The present inventors focused on adultpatients for whom the definition of SIRS is clear and examined theability of the signature to distinguish between adult patients withbacterial sepsis and those with viral infections as well as betweenadult patients with bacterial sepsis and those with viral sepsis.

Adult patients with bacterial sepsis were defined according to theAmerican College of Chest Physicians and the Society of Critical CareMedicine. SIRS was defined by the presence of at least two of thefollowing findings: (i) body temperature <36° C. or >38° C., (ii) heartrate >90 beats per minute, (iii) respiratory rate >20 breaths per minuteor, on blood gas, a PaCO₂<32 mm Hg (4.3 kPa), and (iv) WBC<4,000cells/mm³ or >12,000 cells/mm³ or >10% band forms. It was found that thesignature achieved very high levels of accuracy in distinguishingbetween adult patients with bacterial sepsis and those with viral sepsis(AUC of 0.97 and 0.93 for the Unanimous [adult bacterial sepsis, adultviral sepsis] and the Majority [adult bacterial sepsis, adult viralsepsis] cohorts, respectively). These results demonstrate the utility ofthe signature in differentiating adult patients with bacterial sepsisfrom adult patients with viral infections.

TABLE 6 Signature accuracy in diagnosing bacterial sepsis vs. viralsepsis in adult patients Viral Bacterial Total AUC patients, n patients,n patients, n [95% CI] 21 93 114 [0.94, 1.00] 0.97 Unanimous 35 112 147[0.89, 0.97] 0.93 Majority

Bacterial vs. non-bacterial patients stratification: Antibiotic misusetypically stems from the use of these drugs to treat non-bacterial(viral or non-infectious) patients or due to delayed or missed diagnosisof bacterial infections.

Therefore, the present inventors further examined the signatureperformance for distinguishing between bacterial and non-bacterialpatients. The entire Majority cohort was evaluated using leave-10%-outcross-validation, yielding an AUC of 0.94±0.02. Improved performanceswere shown when evaluating the Unanimous cohort (AUC of 0.96±0.02), andafter filtering out patients with a marginal immune response (Table 7).

TABLE 7 Signature measures of accuracy for diagnosing bacterial vs.non-bacterial (viral and non-infectious) patients. B. Marginal immuneresponse filter A. All patients Majority Unanimous Majority UnanimousAccuracy cohort cohort cohort cohort measure 0.95 0.96 0.94 0.96 AUC(0.93, 0.97) (0.94, 0.98) (0.92, 0.96) (0.94. 0.98) 0.91 0.93 0.88 0.91Total (0.89, 0.93) (0.91, 0.95) (0.85, 0.91) (0.89, 0.93) accuracy 0.910.92 0.87 0.88 Sensitivity (0.88, 0.95) (0.88, 0.95) (0.83, 0.91) (0.85,0.91) 0.92 0.94 0.90 0.93 Specificity (0.89, 0.95) (0.91, 0.96) (0.87,0.93) (0.91, 0.95) 11.4 15.3 8.7 12.6 LR+ (8, 16) (10, 23) (6, 12) (9,18) 0.1 0.08 0.14 0.13 LR− (0.07, 0.14) (0.05, 0.13) (0.11, 0.19) (0.09,0.18) 116 180 60 97 DOR (67, 200) (94, 344) (38, 94) (56, 168) A.Performance estimates and their 95% CIs were obtained using aleave-10%-out cross-validation on all patients in the Unanimous(n_(Bacterial) = 256, n_(Non-bacterial) = 383), and Majority(n_(Bacterial) = 319, n_(Non-bacterial) = 446) cohorts. B. The analysiswas repeated after filtering out patients with a marginal immuneresponse (Unanimous [n_(Bacterial) = 237, n_(Non-bacterial) = 343,n_(Marginal) = 59], and Majority [n_(Bacterial) = 292, n_(Non-bacterial)= 387, n_(Marginal) = 86]), which resembles the way clinicians arelikely to use the signature.

Protein stability at different temperatures can affect the assayperformance: The utility of a biomarker depends on its stability inreal-life clinical settings (e.g., its decay rate when the sample isstored at room temperature prior to analyte measurement). To addressthis, we examined the stability of TRAIL, CRP and IP-10 in serum samplesfrom four independent individuals during 24 hours at 4° C. and 25° C.Aliquots of 100 μL from each plasma sample were pipetted into 0.2 mLtubes and kept at 4° C. or 25° C. from 0 to 24 hours. Subsequently, thelevels of the analytes were measured (different time-points of the sameanalytes were measured using the same plate and reagents). The analytehalf-lives at 4° and 25° C. were greater than 72 hours for TRAIL, CRPand IP-10 (FIGS. 15A-15C). Of note, in the real clinical setting, if thesamples are stored at room temperature, the concentrations of TRAIL,IP-10 and CRP should be measured within about 24 after the sample isobtained. Preferably they should be measured within 5 hours, 4 hours, 3hours, 2 hours, 1 hour, or even immediately after the sample wasobtained. Alternatively, the sample should be stored at a temperaturelower than 10° C., and then TRAIL can be measured more than 24 afterobtaining the sample.

The three protein combination outperforms any individual and pairs ofproteins: The combination of the three proteins outperforms that of theindividual and pairs of proteins for distinguishing bacterial vs. viraland infectious vs. non-infectious patients.

TABLE 8 Bacterial vs. viral Proteins Protein Protein AUC #3 #2 #1 0.89 —— TRAIL 0.88 — — CRP 0.66 — — IP-10 0.95 — CRP TRAIL 0.93 — IP-10 CRP0.90 — IP-10 TRAIL 0.96 IP-10 CRP TRAIL

TABLE 9 Infectious vs. Noninfectious Proteins Protein Protein AUC #3 #2#1 0.60 — — TRAIL 0.87 — — CRP 0.89 — — IP-10 0.90 — CRP TRAIL 0.95 —IP-10 CRP 0.89 — IP10 TRAIL 0.96 IP-10 CRP TRAIL

Performance analysis as a function of the prevalence of bacterialinfections: The prevalence of bacterial and viral infections is settingdependent. For example, in the winter, a pediatrician in the outpatientsetting is expected to encounter substantially more viral infectionsthan a physician in the hospital internal department during the summer.Notably, some measures of diagnostic accuracy such as AUC, sensitivity,and specificity are invariant to the underlying prevalence, whereasother measures of accuracy, such as PPV and NPV are prevalencedependent. In this section, the expected signature performance in termsof PPV and NPV in clinical settings with different prevalence ofbacterial and viral infections is reviewed.

As the basis for this analysis the signature accuracy measures were usedthat were obtained using the Unanimous (bacterial, viral) and Majority(bacterial, viral) cohorts. The prevalence of bacterial infections inthe Unanimous cohort was 51.7% yielding a PPV of 93%±3% and NPV of93%±3%. The prevalence of bacterial infections in the Majority cohortwas 48.7% yielding a PPV of 89%±3% and NPV of 92%±3%.

The measured sensitivity and specificity was used to compute theexpected changes in the signature PPV and NPV as a function of theprevalence of bacterial infections (FIGS. 14A-14B).

Examples of different clinical settings and the extrapolated signaturePPV and NPV for each of them are presented in Table 10A.

TABLE 10A Extrapolated signature PPV and NPV in different clinicalsettings, based on the Unanimous cohort. Prevalence of Bacterial NPV PPVinfections* Age Setting 98% 76% 20% Children Outpatient 97% 85% 35%Adults Outpatient 94% 93% 50% Children Inpatient 78% 98% 80% AdultsInpatient *An average annual prevalence. Estimates of bacterialinfection prevalence are based on data reported in the Bacterialetiology chapter, Part 7 of Harrison's Internal Medicine 17^(th)Edition.

The signature outperforms standard laboratory and clinical parametersfor diagnosing bacterial vs. viral infections: Standard laboratory andclinical parameters, some of which are routinely used in clinicalpractice to aid in the differential diagnosis of an infection source,were evaluated in the Majority cohort (bacterial, viral, non-infectious,n=765). The evaluated parameters included ANC, % neutrophils, %lymphocytes, WBC, and maximal temperature. In accordance with thewell-established clinical role of these parameters, we observed astatistically significant difference in their levels between bacterialand viral patients (FIGS. 15A-15E). For example, bacterial patients hadincreased levels of ANC (P<10⁻²⁴), and WBC (P<10⁻¹⁰), whereas viralpatients had a higher % lymphocytes (P<10⁻³¹). The signature wassignificantly more accurate than any of the individual features(P<10⁻¹⁸) and their combinations (P<10⁻¹⁵), see FIG. 3A).

The signature outperforms protein biomarkers with a well-establishedimmunological role: The signature outperformed all clinical parametersand the 600 proteins that were evaluated during the screening phase (seeFIGS. 3A-3B). The following section further compares the signature toselected proteins that are routinely used in the clinical setting orthat have an immunological role.

One of the most widely used and useful protein biomarkers fordifferentiating sepsis from other non-infectious causes of SIRS incritically ill patients is procalcitonin (PCT). Whether PCT can be usedto distinguish between local bacterial and viral infections is lessclear. To test this, we measured PCT concentrations in 76 randomlyselected patients from the Unanimous (bacterial, viral) cohort(n_(Bacterial)=39, n_(Viral)=37) and 101 randomly selected patients fromthe Majority (bacterial, viral) cohort (n_(Bacterial)=51, n_(Viral)=50)and compared the diagnostic accuracy based on PCT levels to that of thesignature. PCT accuracy was calculated using the standard cutoffsroutinely applied in the clinical setting (0.1 ng/mL, 0.25 ng/mL, 0.5ng/mL, and 1 ng/mL.¹⁹⁻²³ Maximal PCT sensitivity of 69% was attained ata cutoff of 0.1 mg/mL and resulted in a specificity of 62% (for theUnanimous [bacterial, viral] cohort). For the same cohort, the signatureshowed significantly higher sensitivity of 94% (P<0.001) and specificityof 93% (P<0.001) (FIG. 16A). A comparison using the patients from theMajority (bacterial, viral) cohort showed similar results (FIG. 16B).

Overall, despite its high diagnostic and prognostic value for sepsisdetection in critically ill patients, our results indicate that PCT isless accurate in distinguishing between patients with local infections(bacterial vs. viral).

Another protein biomarker used in the clinical setting is the C-reactiveprotein (CRP), an acute phase response protein that is up-regulated ininfections and other inflammatory conditions. The performance of CRP wascompared to that of the signature using the entire Unanimous (bacterial,viral) and Majority (bacterial, viral) cohorts. CRP accuracy wasdetermined using several standard cutoffs applied in the clinicalsetting.²⁴⁻²⁶ Maximal CRP sensitivity of 92% was attained at 20 mg/mLcutoff resulting in a specificity of 60% (for the Unanimous [bacterial,viral] cohort) (FIG. 17A). The signature had a similar sensitivity (94%)and a significantly higher specificity (93%, P<10⁻⁹) in the same cohort.Similar results were observed using the Majority (bacterial, viral)cohort (FIG. 17B). Overall, the signature has a similar sensitivity toCRP with a 20 mg/L cutoff but a considerably higher specificity fordistinguishing bacterial from viral patients.

Next, the differential response of protein biomarkers with awell-established role in the host response to infections was examined(Table 10B and FIGS. 18A-18H). Each biomarker was tested on at least 43patients (about half bacterial and half viral), and if it showedpromising results, it was further tested on additional patients (up to150).

TABLE 10B A list of protein biomarkers with a well-established role inthe host response against infections, and the number of patients used totest each biomarker (for each analysis the analyzed patients includedapproximately half bacterial and half viral patients). No. of Proteinpatients Short description biomarker 120 CD11a is expressed by allleukocytes as part of the integrin CD11a lymphocyte function-associatedantigen-1 (LFA-1). LFA-1 plays a central role in leukocyte intercellularadhesion through interactions with its ligands, ICAMs 1-3 (intercellularadhesion molecules 1 through 3). CD11a also functions in lymphocyteco-stimulatory signaling. 79 CD11C is an integrin α X chain protein andmediates cell-cell CD11C interactions during inflammatory responses. 82CD80 is a membrane receptor involved in the co-stimulatory signal CD80essential for T-lymphocyte activation. The binding of CD28 or CTLA-4 toCD80 induces T-cell proliferation and cytokine production. 65 These areMHC class I antigens associated with β2-microglobulin HLA-A, B, C andare expressed by all human nucleated cells. HLA-A, B, C are central incell-mediated immune response and tumor surveillance. 49 IFN-γ is asoluble cytokine. IFN-γ participates in innate and adaptive IFN-γimmunity against viral and intracellular bacterial infections and intumor control. 43 IL-1a is a member of the IL-1 cytokine family. IL-1ais a pleiotropic IL-1a cytokine involved in various immune responses,inflammatory processes, and hematopoiesis. IL-1a is produced bymonocytes and macrophages as a proprotein, which is proteolyticallyprocessed and released in response to cell injury, thereby inducingapoptosis. 49 IL-2 is produced by T-cells in response to antigenic ormitogenic IL-2 stimulation. IL-2 is required for T-cell proliferationand other activities crucial for regulation of the immune response. 43IL-6 is a cytokine that functions in inflammation and maturation of BIL-6 cells. IL-6 is an endogenous pyrogen capable of inducing fever inpeople with autoimmune diseases or infections. 43 IL-8 is a member ofthe CXC chemokine family and functions as one IL-8 of the majormediators of the inflammatory response. 43 IL-9 is a cytokine that actsas a regulator of a variety of hematopoietic IL-9 cells. IL-9 supportsIL-2 independent and IL-4 independent growth of helper T-cells. 48 IL-10is a cytokine produced primarily by monocytes and to a lesser IL-10extent by lymphocytes. IL-10 has pleiotropic effects in immunoregulationand inflammation. 49 IL-15 is a cytokine that stimulates theproliferation of T-lymphocytes. IL-15 49 IL-16 functions as achemo-attractant, a modulator of T cell IL-16 activation, and aninhibitor of HIV replication. 54 sTNFRSF1A is a receptor forTNFSF2/TNF-α and homo-trimeric sTNFRSF1A TNFSF1/lymphotoxin-α thatcontributes to the induction of non- cytocidal TNF effects includinganti-viral state and activation of the acid sphingomyelinase. 43 TNF-αis a cytokine secreted mainly by macrophages. TNF-α can TNF-α inducecell death of certain tumor cell lines. It is a potent pyrogen causingfever directly or by stimulation of IL-1 secretion. 43 TNF-β is a potentmediator of inflammatory and immune responses. TNF-β It is produced byactivated T and B lymphocytes and is involved in the regulation ofvarious biological processes including cell proliferation,differentiation, apoptosis, lipid metabolism, coagulation, andneurotransmission. 150 TREM is a pro-inflammatory amplifier present onneutrophils and TREM monocytes.

Since these biomarkers do not have a well-established cutoff in theclinical setting, we used their AUCs as a basis for comparison (FIG. 3B)The most informative biomarker was TREM (AUC of 0.68±0.09). The accuracyof TREM was significantly lower than that of the signature (P<10⁻⁹ whencomparing the two AUCs; FIG. 3B). These results demonstrate that mereparticipation of a protein in the host response to an infection does notnecessarily imply diagnostic utility. For example, although IFN-γ has awell-established role in the immune response to viruses andintracellular bacteria, its short half-life (<20 h)²⁷ limits itsdiagnostic utility (as its concentration in the blood is highlydependent on the time from infection onset).

Example 3 Trinary Classifier Outperforms a Binary Classifier

In the binary model the classifier is trained by classifying all samplesas either ‘Bacterial’ or ‘Non-bacterial’ (‘Viral’ and ‘Non-infectious’are grouped). In the trinary model, the classifier learns to distinguishbetween three classes ‘Bacterial’, ‘Viral’ and ‘Non-infectious’. Theprobability of the viral and the non-infectious are then groupedtogether to give the probability of ‘non-bacterial’. This wasdemonstrated on the present data.

Both of the above classifiers were evaluated using a leave 10%-outcross-validation on both the Majority and Unanimous cohorts.

Results

Running the binary classifier on the majority cohort yields the resultsas summarized in Table 10C, herein below:

TABLE 10C Reference class Viral and non- Bacterial infectious (B) (V +NI) 63 411 V + NI 256 35 B

The sensitivity of the classifier on the Majority cohort is 80.3% andthe specificity is 92.2%.

Running the multinomial based classifier on the same dataset yields thefollowing results summarized in Table 10D.

TABLE 10D Reference class (B) (V + NI) 54 417 V + NI 265 29 B

It can be seen that this classifier outperforms the previous one both interms of sensitivity and in terms of specificity. The sensitivity wasimproved to 83.1% and the specificity to 93.5%.

Running the binary classifier on the Unanimous cohort yields the resultssummarized in Table 11.

TABLE 11 Reference class (B) (V + NI) 39 358 V + NI 217 25 B

The sensitivity of the classifier on the Unanimous cohort is 84.8% andthe specificity is 93.5%.

Running the multinomial based classifier on the same dataset yields theresults summarized in Table 12.

TABLE 12 Reference class (B) (V + NI) 38 364 V + NI 218 19 B

This classifier outperforms the previous one both in terms ofsensitivity and in terms of specificity. The sensitivity was improved to85.2% and the specificity to 95.0%.

In summary, the trinary classifier outperforms the binary basedclassifier both in terms of sensitivity and in terms of specificity onboth datasets tested.

Example 4 The Clinical Accuracy of the Signature Remains Robust Evenwhen Analytical Accuracy is Reduced

It is important to assess how clinical accuracy is affected by theincrease in the CV (std/mean) of the proteins measurements, becauseoften different measurement devices, particularly those that are usefulat the point-of-care, show increased CVs (i.e. reduced analyticalaccuracy).

The present inventors examined the change in AUC of the signature fordistinguishing bacterial from viral infection as a function of theincrease in CV of both TRAIL and CRP. This was done by taking theoriginal patient data of the Unanimous cohort and simulating an increasein CV using monte-carlo simulations (FIGS. 19A-19B). Specifically, foreach combination of TRAIL and CRP CVs, 100 simulated measurements wereassigned to each of the patients and the AUC in each case wasrecomputed. The average AUC per CV combination is depicted. It can beseen that the signature clinical accuracy (in terms of AUC) is robust tothe increases in technical CV. For example, increasing the ELISA CV by0, 0.24 and 0.4 leads to a reduction in AUCs of 0.96, 0.95 and 0.94respectively. Similar results are obtained when increasing the CV ofIP-10, and when repeating the simulations on the Majority cohort.

This result may be explained by the usage of multiple biomarkers thatcompensate for one another. This surprising finding is useful because itopens the way to perform measurements of the proteins on cheap and rapidtechnologies (such as POC technologies), which often show reducedanalytical sensitivity (compared for example to automated immunoassaysor ELISA), without losing clinical accuracy.

Example 5

Different ELISA protocols can be applied for measuring TRAIL and IP-10,which would lead to different signal to noise ratios, andconsequentially to different concentrations being measured. Morespecifically, while the overall trend of the biomarkers will bepreserved regardless of the protocol (e.g. TRAIL increases in viralinfections and decreases in bacterial), the measurement scale isprotocol dependent. In the following subsections, examples of protocolsare described that lead to different measured concentrations of IP-10and TRAIL.

Measurements of soluble IP-10 and TRAIL using ELISA—Protocol no. 1: Todetermine the concentrations of soluble IP-10 and TRAIL in human plasmaand serum samples, a standard Sandwich ELISA (Enzyme-linkedimmunosorbent assay) was used. Briefly, the wells of 96-well plate werecoated with capture-antibody specific to TRAIL and IP-10 and diluted incoating buffer (e.g. 1×PBS) followed by overnight incubation at 4° C.The wells were washed twice with washing buffer (e.g. 1×PBS with 0.2%Tween-20) and subsequently blocked with blocking buffer containingproteins (e.g. 1×PBS with 0.2% Tween-20 and 5% non-fat milk) for atleast 2 hours at room temperature or overnight at 4° C. Wells were thenwashed twice with washing buffer. Protein standards and plasma or serumsamples were incubated for two hour at room temperature. Then, the wellswere washed three times with a washing buffer and subsequently incubatedwith an HRP conjugated detection-antibody specific to TRAIL and IP-10,diluted in blocking buffer for two hours at room temperature.

The wells were washed four times with a washing buffer and incubatedwith a reaction solution that contained an HRP substrate (e.g. TMB;3,3′,5,5′-Tetramethylbenzidine). After adequate color development, astop solution was added to each well. The absorbance of the HRP reactionproduct in 450 nm was determined using standard spectrophotometer. Thisprotocol took 5 (TRAIL) and 4.75 (IP10) hours respectively and isreferred to herein as the slow protocol.

Measurements of Soluble IP-10 and TRAIL Using ELISA— Protocol No. 2:

Reducing assay time allows for increased clinical utility. To furtherreduce the protocol run time, the protocol was optimized for measuringTRAIL and IP10 and reduced to less than 100 minutes. The rapid protocolwas performed as follows:

50 μl of assay diluent and 50 μl of Standards was added to samples orcontrols per well. The reaction was incubated for 30 minutes at roomtemperature on a horizontal orbital microplate shaker (3 mm orbit) setat 550 rpm. Each well was then aspirated and washed four times by usinga wash buffer. Next, 200 μl of Conjugate was added to each well and thereactions were incubated for 45 minutes at room temperature on theshaker. The wells were washed four times with a washing buffer andincubated with a reaction solution that contained an HRP substrate (e.g.TMB; 3,3′,5,5′-Tetramethylbenzidine). After 10-25 minutes, a stopsolution was added to each well. The absorbance of the HRP reactionproduct in 450 nm was determined using a standard spectrophotometer.This protocol took 99 (TRAIL) and 85 (IP-10) minutes respectively and isreferred to herein as the rapid protocol.

The slow and the rapid protocol measurements were compared using 357samples for TRAIL and 189 samples for IP-10, and showed highlycorrelated results (FIGS. 30A-30B).

Of note, the average TRAIL concentration obtained using the rapidprotocol was roughly 70 percent less than that obtained using the slowprotocol concentration. Such alterations in measured concentrations ofproteins across different protocols often occur and can be compensatedfor by correlating the measurements of the two protocols and computing atransformation function. For example, the transformation functiony_slow=0.709'y_rapid−3e-12 may be used to translate the concentrationsof the rapid protocol and the slow protocol. This translation preservesTRAIL's accuracy. Other, translation functions and protocols can bedeveloped by one skilled in the art that also preserve the accuracy. Insummary, the behavior of TRAIL remains the same across the two protocols(i.e. highest in viral, lower in non-infectious and lowest inbacterial), despite a shift in the calculated concentrations.

Different Protocols and Cohorts Lead to Different Model Coefficients:

An example of the multinomial logistic model coefficients generated onthe majority patients cohort when measuring IP-10 and TRAIL with theslow protocol is shown in Table 13:

TABLE 13 Second Coordinate First Coordinate δ₁ (bacterial) δ₀ (viral) b₀= −1.5389 ± 0.75676 a₀ = 1.7331 ± 0.62936 Const b₁ = 0.0851 ± 0.015288a₁ = 0.0514 ± 0.014896 CRP (mg/ml) b₂ = 0.0046 ± 0.001372 a₂ = 0.0049 ±0.001372 IP10 (pg/ml) b₃ = −0.0155 ± 0.007056 a₃ = 0.0048 ± 0.005096TRAIL (pg/ml)

An example of the multinomial logistic model coefficients generated onthe consensus patients cohort when measuring IP-10 and TRAIL with theslow protocol is shown in Table 14.

TABLE 14 Second Coordinate First Coordinate δ₁ (bacterial) δ₀ (viral) b₀= 2.6091 ± 0.9357 a₀ = 2.6866 ± 0.75048 Const b₁ = 0.0866 ± 0.016856 a₁= 0.0499 ± 0.016464 CRP (mg/ml) b₂ = 0.0052 ± 0.001568 a₂ = 0.0059 ±0.001568 IP10 (pg/ml) b₃ = −0.0115 ± 0.008232 a₃ = 0.0084 ± 0.005684TRAIL (pg/ml)

Since the frequency of the subgroups in the patient cohort deviates fromthe anticipated frequency in the general population, one can furtheradjust the model coefficients to reflect a predetermined priorprobability using standard techniques for coefficient adjustment (forexample see G. King and L Zeng, Statistics in Medicine 2002). Forexample, the following examples show multinomial logistic modelcoefficients generated on the majority patients cohort when measuringIP-10 and TRAIL with the slow protocol, reflecting prior probability of45% bacterial, 45% viral and 10% non-infectious.

Model coefficients (trained on majority cohort) after prior adjustmentare summarized in Table 15:

TABLE 15 Second Coordinate First Coordinate δ₁ (bacterial) δ₀ (viral) b₀= −1.1302 ± 0.75676 a₀ = −1.4151 ± 0.62936 Const b₁ = 0.0851 ± 0.015288a₁ = 0.0514 ± 0.014896 CRP (mg/ml) b₂ = 0.0046 ± 0.001372 a₂ = 0.0049 ±0.001372 IP10 (pg/ml) b₃ = −0.0155 ± 0.007056 a₃ = 0.0048 ± 0.005096TRAIL (pg/ml)

Model coefficients (trained on consensus cohort) after prior adjustmentare summarized in Table 16.

TABLE 16 Second Coordinate First Coordinate δ₁ (bacterial) δ₀ (viral) b₀= −1.7833 ± 0.9357 a₀ = −2.083 ± 0.75048 Const b₁ = 0.0866 ± 0.016856 a₁= 0.0499 ± 0.016464 CRP (mg/ml) b₂ = 0.0052 ± 0.001568 a₂ = 0.0059 ±0.001568 IP10 (pg/ml) b₃ = −0.0115 ± 0.008232 a₃ = 0.0084 ± 0.005684TRAIL (pg/ml)

Of note, other combinations of coefficients can be chosen to producesimilar results, as would be evident to one skilled in the art. Otherprotocols for measuring proteins that affect the measured proteinconcentrations would yield different model coefficients. For example,the rapid protocol for measuring TRAIL reduces the computedconcentrations to roughly 70% of the concentrations computed in the slowprotocol. Thus, one way to adjust for this is to alter the modelcoefficients of TRAIL to account for this change. Another way is todivide the rapid protocol measurements of TRAIL by 70% and plug in tothe above mentioned models that were developed for the slow protocol.

It is often preferable to use a log transformation on the proteinmeasurements in order to improve model accuracy and calibration (i.e.better fit between the predicted risk of a certain infection and theobserved risk).

An example of a model with log transformation of TRAIL and IP-10 isdepicted in Table 17 (model was trained on the consensus cohort):

TABLE 17 Second Coordinate First Coordinate δ₁ (bacterial) δ₀ (viral) b₀= −5.9471 ± 3.3391 a₀ = −14.8487 ± 3.3839 Const b₁ = 0.0833 ± 0.016856a₁ = 0.0437 ± 0.017052 CRP (mg/ml) b₂ = 1.3868 ± 0.48608 a₂ = 2.0148 ±0.4408 IP10 (pg/ml) b₃ = −0.788 ± 0.60505 a₃ = 0.8946 ± 0.61348 TRAIL(pg/ml)

Example 6 Hypersurface Parameterization

Given the concentrations of CRP [C], TRAIL [T] and IP-10 [P] we define:

δ₀=−1.299+0.0605×[C]+0.0053×[P]+0.0088×[T]

δ₁=−0.378+0.0875×[C]+0.0050×[P]−0.0201×[T]

The probabilities can then be calculated by:

${P({Viral})} = \frac{e^{\delta_{0}}}{1 + e^{\delta_{0}} + e^{\delta_{1}}}$${P({Bacterial})} = \frac{e^{\delta_{0}}}{1 + e^{\delta_{0}} + e^{\delta_{1}}}$${P\left( {{Non} - {infectious}} \right)} = \frac{1}{1 + e^{\delta_{0}} + e^{\delta_{1}}}$

We define the hyper surface in the [C], [T], [P] space:

$\frac{e^{\delta_{1}}}{1 + e^{\delta_{0}} + e^{\delta_{1}}} = \omega$

that is used to distinguish between bacterial and non-bacterialpatients. In one preferred embodiment. In other preferred embodiments.Given a patient's [C], [T], [P] values that patient is classified asbacterial if

${\frac{e^{\delta_{1}}}{1 + e^{\delta_{0}} + e^{\delta_{1}}} > \omega},$

else he/she are classified as non-bacterial.

We define the set all hyper plains that can be used to distinguishbetween bacterial and non-bacterial infections as those that residewithin the following two hyper surfaces:

$\frac{e^{\delta_{1}}}{1 + e^{\delta_{0}} + e^{\delta_{1}}} = {\omega + \epsilon_{1}}$$\frac{e^{\delta_{1}}}{1 + e^{\delta_{0}} + e^{\delta_{1}}} = {\omega - \epsilon_{0}}$

ϵ₁ can be any number between 0 and 1−. In some preferred embodiments ϵ₁is smaller then 0.5, 0.4, 0.3, 0.2 or 0.1.ϵ₀ can be any number between 0 and ω. In some preferred embodiments ϵ₀is smaller then 0.5, 0.4, 0.3, 0.2 or 0.1.

Illustrated examples of surfaces are provided in Example 7.

Example 7 Graphical Representation of Classification

FIG. 20 is a 3-dimensional visualization of bacterial (‘+’), viral (‘o’)and non-infectious (‘{circumflex over ( )}’) patients. Differentpatients types are mapped to distinct regions in the CRP (μg/ml), TRAILand IP-10 (pg/ml) concentration map.

By way of example probability surfaces were generated using amultinomial logistic regression. Contour plots of the surfaces are shownin FIGS. 21A-28C, as a function of TRAIL (y-axis), CRP (x-axis), andIP-10 concentrations. FIGS. 21A, 22A, 23A, 24A, 25A, 26A, 27A, 28A, showprobabilities of viral infectious, FIGS. 21B, 22B, 23B, 24B, 25B, 26B,27B, 28B, show probabilities of bacterial or mixed infectious, and FIGS.21C, 22C, 23C, 24C, 25C, 26C, 27C, 28C, show probabilities ofnon-infectious or healthy. FIGS. 21A-21C correspond to IP10_(p)g rangingfrom 0 to 100, FIGS. 22A-22C correspond to IP10_(p)g ranging from 100 to200, FIGS. 23A-23C correspond to IP10_(p)g ranging from 200 to 300,FIGS. 24A-24C correspond to IP10_(p)g ranging from 300 to 400, FIGS.25A-25C correspond to IP10_(p)g ranging from 400 to 500, FIGS. 26A-26Ccorrespond to IP10_(p)g ranging from 500 to 1000, FIGS. 27A-27Ccorrespond to IP10_(p)g ranging from 1000 to 2000 and FIGS. 28A-28Ccorrespond to IP10_(p)g which is 2000 or above.

Patients with bacterial or mixed are marked with a ‘+’; viral with a ‘o’and non-infectious or healthy with a ‘{circumflex over ( )}’. It can beseen in that low levels of IP-10 are associated with non-infectiousdisease, higher levels with bacterial and highest with viral. Low levelsof TRAIL are associated with bacterial infections, higher withnon-infectious and healthy, and highest with viral. Low levels of CRPare associated with non-infectious disease and healthy subjects, higherwith viral infection and highest with bacterial. The combination of thethree proteins generates a probability function whose diagnosticperformance outperforms any of the individual or pairs of proteins.

FIGS. 35A-35D are contour plots describing the probability of bacterial(FIG. 35A), viral (FIG. 35B), non-bacterial (FIG. 35C), andnon-infectious (FIG. 35D) etiologies as a function of the coordinates δ₀and δ₁. The probability values range between 0% (black) to 100% (white).

Example 8 Exemplified Protocols for Measuring Expression Levels

In general, without limitation expression value of TRAIL can be measuredusing an ELISA or automated immunoassay; expression value of IP-10 canbe measured using an ELISA assay; and expression value of CRP can bemeasured using an ELISA or automated immunoassay. The expression valueof CRP can also be measured using a functional assay based on itscalcium-dependent binding to phosphorylcholine.

Protocol A: Suitable Protocol for Measuring an Expression Value of TRAIL

(a) immobilize TRAIL present in a sample using an antibody to a solidsupport;(b) contact immobilized TRAIL with a second antibody that specificallybinds to TRAIL; and(c) quantify the amount of antibody that binds to the immobilized TRAIL.

Suitable Protocol for Measuring an Expression Value of IP-10

(a) immobilize IP-10 present in a sample using a capture antibody to asolid support;(b) contact immobilized IP-10 with a second antibody that specificallybinds to IP-10; and(c) quantify the amount of antibody that binds to the immobilized IP-10.

Suitable Protocol for Measuring an Expression Value of CRP

(a) immobilize CRP present in a sample using a capture antibody to asolid support;(b) contact immobilized CRP with a second antibody that specificallybinds to I CRP; and(c) quantify the amount of antibody that binds to the immobilized CRP.

Protocol B: Suitable Protocol for Measuring an Expression Value of TRAIL

(a) Incubate a sample with a first antibody that specifically binds toTRAIL, wherein the said first antibody is immobilized to a solid phase;

(b) Wash;

(c) Add second antibody that specifically binds to TRAIL, wherein thesecond antibody is conjugated to an enzyme; wash(d) Add enzyme substrate and quantify the amount of antibody that bindsto the immobilized sample.

Suitable Protocol for Measuring an Expression Value of IP-10

(a) Incubate a sample with a first antibody that specifically binds toIP-10, wherein the said first antibody is immobilized to a solid phase;

(b) Wash;

(c) Add second antibody that specifically binds to IP-10, wherein thesecond antibody is conjugated to an enzyme; wash(d) Add enzyme substrate and quantify the amount of antibody that bindsto the immobilized sample.

Suitable Protocol for Measuring an Expression Value of CRP

(a) Incubate a sample with a first antibody that specifically binds toCRP, wherein the said first antibody is immobilized to a solid phase;

(b) Wash;

(c) Add second antibody that specifically binds to CRP, wherein thesecond antibody is conjugated to an enzyme; wash(d) Add enzyme substrate and quantify the amount of antibody that bindsto the immobilized sample.

Protocol C: Suitable Protocol for Measuring an Expression Value of CRP

(a) measure the turbidity of a mixture of lipids;(b) contact sample with a known amount of the lipids (preferablyphosophorylcholine) in the presence of Calcium; and(c) measure the turbidity of the solution, wherein increase in turbiditycorrelates with the amount of CRP.

Example 9 Detailed Description of ELISA for Analyzing the Amount ofTRAIL and IP-10

Sample collection and storage: Exposure of samples to room temperatureshould be minimized (less than 6 hours). A serum separator tube (SST) isused and the samples are allowed to clot for at least 30 minutes beforecentrifugation (5 minutes at 1200×g). Serum may be assayed immediately,or aliquoted and stored at 4-8° C. for up to 24 hours or at ≤−20° C. forup to 3 months. Repeated freeze-thaw cycles should be avoided.

Reagent preparation: All reagents should be brought to room temperaturebefore use.

Substrate solution: Color Reagents A and B should be mixed together inequal volumes within 10 minutes of use. Protect from light.

QC-1V, QC-2B and Standards: Thaw all QC and Standards and remove 150 uLfrom each vial to a separate marked Polypropylene test tube. Move backto −20° C. immediately after use.

Trail Measurements:

The materials used for analyzing TRAIL are provided in Table 18, hereinbelow.

TABLE 18 Storage conditions Description Part Store at 96 well microplate(12 strips of 8 wells) TRAIL 2-8° C. coated with anti-TRAIL antibodyMicroplate 21 ml of anti-TRAIL specific antibody TRAIL conjugated tohorseradish peroxidase with Conjugate preservatives 11 ml of a bufferedprotein base with Assay preservatives diluent MM1S 21 mL of a 25-foldconcentrated solution Wash Buffer of buffered surfactant withpreservative Concentrate 12 mL of stabilized hydrogen peroxide Colorreagent A 12 mL of tetramethylbenzidine (TMB) Color reagent B 6 mL of 2Nsulfuric acid Stop solution 4 adhesive strips Plate sealer Store 6 vialscontaining 0.7 ml of recombinant 6 TRAIL at −20 C. ° human TRAIL inbuffered protein base with Standards immediately preservatives at thefollowing concentrations after 500, 250, 125, 62.5, 31.2 and 0 [pg/mL]receiving. 1 ml QC-1V 1 ml QC-2BTRAIL ELISA procedure

-   a) Prepare samples, reagents and standards as indicated above.-   b) Remove excess microplate strips from the plate frame, return them    to the foil pouch containing the desiccant pack, and reseal.-   c) Add 50 μL of Assay Diluent MM1S to each well.-   d) Add 50 μL of Standard, samples, or QC per well. Cover with the    adhesive strip provided.-   e) Incubate for 30 minutes at room temperature on a microplate    shaker (3 mm orbit) set at 550 rpm.-   f) Aspirate each well and wash, repeating the process 4 times. Wash    by filling each well with Wash Buffer (300 μL). After the last wash,    remove any remaining Wash Buffer by aspirating or decanting. Invert    the plate and blot it against clean paper towels.-   g) Add 200 μL of TRAIL Conjugate to each well. Cover with a new    adhesive strip. Incubate for 45 minutes at room temperature on a    microplate shaker (3 mm orbit) set at 550 rpm.-   h) Repeat the aspiration/wash as in step (g).-   i) Add 200 μL of Substrate solution to each well. Incubate for 24 to    30 minutes at room temperature. Protect from light.-   j) Add 50 μL of Stop solution to each well. The color in the wells    should change from blue to yellow. If the color in the wells is    green or the color change does not appear uniform, gently tap the    plate to ensure thorough mixing.-   k) Determine the optical density of each well immediately, using a    microplate reader set to 450 nm. Set wavelength correction to 570    nm, which will correct for optical imperfections in the plate.

TRAIL calculation of concentrations: Average the duplicate readings foreach sample and subtract the average zero standard optical density(O.D.). Create a standard curve by plotting the mean absorbance for eachstandard (y-axis) against the concentration (x-axis) and draw a best-fitlinear curve. The minimal r² should not fall below 0.96. In case lowerr² values are present, repeat the experiment to get reliable results.

Precision: Precision was evaluated based on the CLSI (formerly NCCLS)EPOS-A2 guidelines. Three samples with concentrations at the low (11.4pg/ml), intermediate (58.8 pg/ml), and high (539.0 pg/ml) physiologicalconcentrations were used to assess precision. Results are summarized inTable 19, where S_(r) is within-run precision and S_(T) is within-deviceprecision:

TABLE 19 High Medium Low (539.0 pg/ml) (58.8 pg/ml) (11.4 pg/ml) 18 1818 # of runs 36 36 36 # of duplicates 13.2 2.45 0.84 S_(r) pg/mL 2.5%4.2% 7.3% S_(r) CV (%) 29.7 3.6 1.3 S_(T) pg/mL 5.5% 6.1% 11.5% S_(T) CV(%)

Recovery: Recovery was evaluated by spiking three levels of humanrecombinant TRAIL (250, 125 and 62.5 pg/mL) into 5 human serum sampleswith no detectable levels of TRAIL. The spiked values and the averagerecovery was then measured and calculated, as shown in Table 20 below.

TABLE 20 Range Average % Recovery Sample 75-78% 77% Serum (n = 5)

Linearity: To assess the linearity of the assay, five clinical samplescontaining high concentrations of TRAIL were serially diluted using aserum substitute to produce samples with values within the physiologicalrange of the assay. Linearity was, on average, 97%, 100% and 105% for1:2, 1:4 and 1:8 dilutions, respectively, as summarized in Table 21below.

TABLE 21 Serum (n = 5)     97% Average % of expected 90-104% Range % 1:2   100% Average % of expected 90-108% Range % 1:4    105% Average % ofexpected 90-121% Range % 1:8

Sensitivity: To estimate the Limitation of Blank (LOB), we tested 72blank samples of serum substitute. The mean of the blank samples was0.78 pg/ml and the standard deviation was 1.39 pg/ml. Therefore, thecalculated LOB is 3.07 pg/ml. To estimate the Limitation of Detection(LOD), the CLSI EP17-A guidelines were followed. Briefly, themeasurement distribution around seven predetermined concentrations werecharacterized, each with 30 independent measurements (210 measurements)yielding an LOD of 10 pg/ml.

Calibration: This immunoassay is calibrated against a purifiedNS0-expressed recombinant human TRAIL.

Expected values: Samples from apparently healthy adult (>18 years) weremeasured for the presence of TRAIL. The range and mean values aresummarized in Table 22.

TABLE 22 Range pg/ml Mean pg/ml Sample Type 17-157 90 Serum (n = 34)

Cross reactivity and interference: This assay recognizes natural andrecombinant human TRAIL. The factors 4-1BB Ligand, APRIL, BAFF/BLyS,CD27 Ligand, CD30 Ligand, CD40 Ligand, Fas Ligand, GITR Ligand, LIGHT,LT α1/β2,LT α2/β1, OPG, OX40 Ligand, TNF-α, TNF-β, TRAIL R3, TRAIL R4,TRANCE and TWEAK were prepared at 50 ng/mL in serum substitution andassayed for cross-reactivity. Additionally, preparations of thesefactors at 50 pg/mL in a mid-range recombinant human TRAIL control weretested for interference. No significant cross-reactivity or interferencewas observed.

IP-10 measurements: The materials used for analyzing IP-10 are providedin Table 23, herein below.

TABLE 23 Storage conditions Description Part Store at 96 well microplate(12 strips of 8 wells) IP-10 2-8° C. coated with anti-IP-10 antibodyMicroplate 21 ml of anti-IP-10 specific antibody IP-10 conjugated tohorseradish peroxidase with Conjugate preservatives 11 ml of a bufferedprotein base with Assay preservatives diluent MM56 21 mL of a 25-foldconcentrated solution Wash Buffer of buffered surfactant withpreservative Concentrate 12 mL of stabilized hydrogen peroxide Colorreagent A 12 mL of tetramethylbenzidine (TMB) Color reagent B 6 mL of 2Nsulfuric acid Stop solution 4 adhesive strips Plate sealer Store 6 vialscontaining 0.7 ml of recombinant 6 IP-10 at −20° C. human IP-10 inbuffered protein base with Standards immediately preservatives at thefollowing concentrations after 1000, 500, 250, 125, 62.5 and 0 [pg/mL]receiving 1 ml QC-1V 1 ml QC-2BIP-10 ELISA procedure

-   a) Prepare samples, reagents and standards as indicated herein    above.-   b) Remove excess microplate strips from the plate frame, return them    to the foil pouch containing the desiccant pack, and reseal.-   c) Add 50 μL of Assay Diluent MM56 to each well.-   d) Add 50 μL of Standard, sample or QC per well. Cover with the    adhesive strip provided.-   e) Incubate for 30 minutes at room temperature on a microplate    shaker (3 mm orbit) set at 550 rpm.-   f) Aspirate each well and wash, repeating the process 4 times. Wash    by filling each well with Wash Buffer (300 μL). After the last wash,    remove any remaining Wash Buffer by aspirating or decanting. Invert    the plate and blot it against clean paper towels.-   g) Add 200 μL of IP-10 Conjugate to each well. Cover with a new    adhesive strip. Incubate for 45 minutes at room temperature on a    microplate shaker (3 mm orbit) set at 550 rpm.-   h) Repeat the aspiration/wash as in step (g).-   i) Add 200 μL of Substrate solution to each well. Incubate for 10    minutes at room temperature. Protect from light.-   j) Add 50 μL of Stop solution to each well. The color in the wells    should change from blue to yellow. If the color in the wells is    green or the color change does not appear uniform, gently tap the    plate to ensure thorough mixing.-   k) Determine the optical density of each well immediately, using a    microplate reader set to 450 nm. Set wavelength correction to 570    nm, which will correct for optical imperfections in the plate.

IP-10 calculation of concentrations: Average the duplicate readings foreach sample and subtract the average zero standard optical density(O.D.). Create a standard curve by plotting the mean absorbance for eachstandard (y-axis) against the concentration (x-axis) and draw a best-fitlinear curve. The minimal r² should not fall below 0.96. In case lowerr² values are present, repeat the experiment to get reliable results.

Precision: Precision was evaluated based on the CLSI (formerly NCCLS)EP05-A2 guidelines. Three samples with concentrations at the low (69.4pg/ml), intermediate (228.2 pg/ml), and high (641.5 pg/ml) physiologicalconcentrations were used to assess precision. Results are summarized inTable 24 where S_(r) is within-run precision and S_(T) is within-deviceprecision:

TABLE 24 High Medium Low (641.5 pg/ml) (228.2 pg/ml) (69.4 pg/ml) 18 1818 # of runs 36 36 36 # of duplicates 21.1 5.6 4.0 S_(r) pg/mL 3.3% 2.4%5.8% S_(r) CV (%) 37.2 12.9 4.9 S_(T) pg/mL 5.8% 5.7% 7.1% S_(T) CV (%)

Recovery: Recovery was evaluated by spiking three levels of human IP-10,500, 250 and 125 pg/mL into 5 human serum samples with no detectablelevels of IP-10. The spiked values and the average recovery was thanmeasured and calculated as illustrated in Table 25 below.

TABLE 25 Range Average % Recovery Sample 72-80% 77 Serum/plasma (n = 5)

Linearity: To assess the linearity of the IP-10 assay, 5 clinicalsamples containing high concentrations of IP-10 ranging between 873.7 to1110.4 pg/mL were serially diluted with a serum substitute to producesamples with values within the physiological range of the assay.Linearity was, on average, 98%, 102% and 104% in 1:2, 1:4 and 1:8dilutions, respectively, as summarized in Table 26 herein below.

TABLE 26 Serum (n = 5)     98% Average % of expected 93-102% Range % 1:2   102% Average % of expected 97-107% Range % 1:4    104% Average % ofexpected 96-111% Range % 1:8

Sensitivity: To estimate the Limitation of Blank (LOB), we tested 72blank samples of serum substitute. The mean of the blank samples was0.23 pg/ml and the standard deviation was 1.26 pg/ml, yielding an LOB of2.29 pg/ml.

To estimate the Limitation of Detection (LOD), the CLSI EP17-Aguidelines were applied. Briefly, the measurement distribution aroundseven predetermined concentrations were characterized, each with 30independent measurements (210 measurements) yielding an LOD of 10 pg/ml.

Calibration: This immunoassay is calibrated against a highly purifiedE-coli-expressed recombinant human IP-10.

Expected values: Samples from apparently healthy adult volunteers weremeasured for the presence of IP-10. The range and mean values are shownin Table 27 below.

TABLE 27 Range pg/ml Mean pg/ml Sample Type 29-525 119 Serum (n = 34)

Cross reactivity and interference: This assay recognizes natural andrecombinant human IP-10. The factors BLC/BCA-1, ENA-78, GCP-2, GROα, GROγ, IFN-γ, IL-8, I-TAC, MIG, NAP-2, SDF-1a and SDF-1β were prepared at 50ng/mL in serum substitution and assayed for cross-reactivity.Additionally, preparations of these factors at 50 pg/mL in a mid-rangerecombinant human IP-10 control were tested for interference. Nosignificant cross-reactivity or interference was observed.

Example 10 Trail and Disease Prognosis

It is often clinically useful to assess patient prognosis, diseaseseverity and outcome. The present inventors found that low levels ofTRAIL are significantly correlated with poor patient prognosis andoutcome, and high disease severity. For example, adult patients in theintensive care unit (ICU) had significantly lower TRAIL levels comparedto all other patients, which were less ill regardless of whether theyhad an infectious or non-infectious etiology. Median serumconcentrations were 9 pg/ml vs. 80 pg respectively, (ranksum P<0.001,FIG. 36A), for severely ill and all other patients respectively.

40 Dutch pediatric patients, 3 months to 5 years of age. The TRAIL serumlevel was measured in 40 Dutch pediatric patients, 3 months to 5 yearsof age. It was found that those patients that were eventually admittedto the ICU (an indication of disease complication and poor prognosis) oreven died had significantly lower TRAIL serum concentrations compared tothe rest of the patients (median of 11 vs. 85, respectively; ranksumP<0.001) as depicted in FIG. 36B. Strikingly, the lowest TRAIL levels(<5 pgml) were measured in the only two children that died in the entirecohort. These results indicate that TRAIL could be used as a prognosticmarker for predicting disease severity and outcome.

Example 11 TRAIL Age and Gender Parameters

Basal levels of TRAIL in healthy individuals or patients with anon-infectious disease are lower in females compared to males duringfertility age (t-test P<0.001) (FIG. 37A), but is invariant in pre- orpost-fertility age (t-test P=0.9, FIG. 37B). This trend was not observedin patients with an infectious disease.

Example 12 Exemplified Manifolds, Hyperplanes and CoordinatesOne-Dimensional Manifold

When n=1, the manifold S is a curved line and the hyperplane π is anaxis defining a single direction δ₁. The coordinate δ₁ in this Exampleis optionally and preferably a linear combination b₀+b₁D₁+b₂D₂+ . . . ,of the polypeptides D₁, D₂, etc.

Table 28 below lists diagnostic performance (in AUCs) attained for n=1.The performance were computed using a leave-10%-out cross validation onthe cohort specified in each row. In rows 1-4, the analyzed subjects hadeither bacterial or viral infections and the coordinate δ₁ wascalculated so that the probabilistic classification function f(δ₁)represented the likelihood that the test subject had a bacterialinfection. In rows 5-8, the analyzed subjects were infectious ornon-infections and the coordinate δ₁ was calculated so that theprobabilistic classification function f(δ₁) represented the likelihoodthat the test subject had an infection. In rows 10-12, the analyzedsubjects had either bacterial or non-bacterial infection and thecoordinate δ₁ was calculated so that the probabilistic classificationfunction f(δ₁) represented the likelihood that the test subject had abacterial infection. In rows 1-4, the columns P and N correspond to thenumber of Bacterial and Viral patients respectively, in rows 5-8, thecolumns P and N correspond to the number of infectious andnon-infectious patients, respectively, and in rows 9-12, the columns Pand N correspond to the number Bacterial and non-Bacterial patientsrespectively. Majority and Consensus indicate the type of cohort onwhich the model was validated.

TABLE 28 N P AUC Polypeptides Cohort No. 334 319 0.93 TRAIL CRP Majority1 334 319 0.94 TRAIL IP-10 CRP Majority 2 271 256 0.95 TRAIL CRPConsensus 3 271 256 0.96 TRAIL IP-10 CRP Consensus 4 112 653 0.93 TRAILCRP Majority 5 112 653 0.96 TRAIL IP-10 CRP Majority 6 112 527 0.93TRAIL CRP Consensus 7 112 527 0.97 TRAIL IP-10 CRP Consensus 8 446 3190.94 TRAIL CRP Majority 9 446 319 0.94 TRAIL IP-10 CRP Majority 10 383256 0.95 TRAIL CRP Consensus 11 383 256 0.96 TRAIL IP-10 CRP Consensus12

Table 29 below lists the coefficients b₀, b₁, b₂, etc that were used todefine the coordinate δ₁, for each of the 12 cases listed in Table 28,respectively. The first coefficient on the left is b₀, and then fromleft to right, the coefficients correspond to the order of thepolypeptides in each row of Table 28. The coefficients correspond to thefollowing concentration scales for each polypeptide: TRAIL (pg/ml),IP-10 (pg/ml) and CRP (ug/ml).

For a given set of polypeptides, the obtained coefficients have smallvariations among the different cohorts. Nevertheless, the coefficientsfor the probabilistic classification functions and coordinates of thepresent embodiments preferably correspond to those obtained for theMajority Cohort.

TABLE 29 Coefficients No. −0.029953 0.027472 0.64814 1 −0.029013−0.00028168 0.028119 0.71542 2 −0.033669 0.034565 0.636 3 −0.03195−0.00058691 0.035748 0.79543 4 0.016837 0.17237 −2.0549 5 0.0052130.00592 0.1263 −2.3344 6 0.018624 0.16625 −2.3469 7 0.0079169 0.00611240.12261 −2.7949 8 −0.027839 0.034954 −0.08503 9 −0.027916 2.2524e−050.034878 −0.088207 10 −0.030997 0.044289 −0.26606 11 −0.03042−0.00018635 0.044938 −0.23907 12

Table 30 below lists diagnostic performance (in AUCs) attained forone-dimensional manifold. The performance were computed using aleave-10%-out cross validation on the Majority cohort. In rows 1-55, theanalyzed subjects had either bacterial or viral infections and theprobabilistic classification function f(δ₁) represented the likelihoodthat the test subject had a bacterial infection. In rows 56-110, theanalyzed subjects were infectious or non-infections and theprobabilistic classification function f(δ₁) represented the likelihoodthat the test subject had an infection. In rows 1-55, the columns P andN correspond to the number of Bacterial and Viral patients respectively,and in rows 56-110, the columns P and N correspond to the number ofinfectious and noninfectious patients, respectively.

TABLE 30 N P AUC Polypeptides No. 141 142 0.88 IL1ra CRP 1 299 295 0.90IP-10 CRP 2 50 51 0.87 PCT CRP 3 241 255 0.90 SAA CRP 4 142 142 0.64IP-10 IL1ra 5 14 19 0.62 PCT IL1ra 6 122 124 0.83 SAA IL1ra 7 142 1420.88 TRAIL IL1ra 8 49 51 0.74 PCT IP-10 9 242 251 0.85 SAA IP-10 10 297295 0.88 TRAIL IP-10 11 40 45 0.78 SAA PCT 12 50 51 0.87 TRAIL PCT 13244 255 0.90 TRAIL SAA 14 141 142 0.90 IP-10 IL1ra CRP 15 14 19 0.82 PCTIL1ra CRP 16 121 124 0.89 SAA IL1ra CRP 17 141 142 0.94 TRAIL IL1ra CRP18 49 51 0.89 PCT IP-10 CRP 19 239 251 0.91 SAA IP-10 CRP 20 40 45 0.88SAA PCT CRP 21 50 51 0.93 TRAIL PCT CRP 22 241 255 0.94 TRAIL SAA CRP 2314 19 0.62 PCT IP-10 IL1ra 24 122 124 0.85 SAA IP-10 IL1ra 25 142 1420.88 TRAIL IP-10 IL1ra 26 13 17 0.76 SAA PCT IL1ra 27 14 19 0.71 TRAILPCT IL1ra 28 122 124 0.92 TRAIL SAA IL1ra 29 39 45 0.81 SAA PCT IP-10 3049 51 0.86 TRAIL PCT IP-10 31 242 251 0.91 TRAIL SAA IP-10 32 40 45 0.86TRAIL SAA PCT 33 14 19 0.83 PCT IP-10 IL1ra CRP 34 121 124 0.92 SAAIP-10 IL1ra CRP 35 141 142 0.94 TRAIL IP-10 IL1ra CRP 36 13 17 0.74 SAAPCT IL1ra CRP 37 14 19 0.90 TRAIL PCT IL1ra CRP 38 121 124 0.94 TRAILSAA IL1ra CRP 39 39 45 0.88 SAA PCT IP-10 CRP 40 49 51 0.92 TRAIL PCTIP-10 CRP 41 239 251 0.94 TRAIL SAA IP-10 CRP 42 40 45 0.92 TRAIL SAAPCT CRP 43 13 17 0.70 SAA PCT IP-10 IL1ra 44 14 19 0.70 TRAIL PCT IP-10IL1ra 45 122 124 0.91 TRAIL SAA IP-10 IL1ra 46 13 17 0.82 TRAIL SAA PCTIL1ra 47 39 45 0.85 TRAIL SAA PCT IP-10 48 13 17 0.82 SAA PCT IP-10IL1ra CRP 49 14 19 0.75 TRAIL PCT IP-10 IL1ra CRP 50 121 124 0.94 TRAILSAA IP-10 IL1ra CRP 51 13 17 0.78 TRAIL SAA PCT IL1ra CRP 52 39 45 0.92TRAIL SAA PCT IP-10 CRP 53 13 17 0.62 TRAIL SAA PCT IP-10 IL1ra 54 13 170.74 TRAIL SAA PCT IP-10 IL1ra CRP 55 87 283 0.91 IL1ra CRP 56 102 5940.96 IP-10 CRP 57 6 101 0.85 PCT CRP 58 78 496 0.91 SAA CRP 59 87 2840.89 IP-10 IL1ra 60 6 33 0.79 PCT IL1ra 61 64 246 0.91 SAA IL1ra 62 87284 0.86 TRAIL IL1ra 63 6 100 0.73 PCT IP-10 64 81 493 0.96 SAA IP-10 65107 592 0.91 TRAIL IP-10 66 3 85 0.89 SAA PCT 67 7 101 0.60 TRAIL PCT 6881 499 0.93 TRAIL SAA 69 87 283 0.95 IP-10 IL1ra CRP 70 6 33 0.76 PCTIL1ra CRP 71 64 245 0.92 SAA IL1ra CRP 72 87 283 0.93 TRAIL IL1ra CRP 736 100 0.81 PCT IP-10 CRP 74 78 490 0.97 SAA IP-10 CRP 75 3 85 0.88 SAAPCT CRP 76 6 101 0.87 TRAIL PCT CRP 77 78 496 0.95 TRAIL SAA CRP 78 6 330.77 PCT IP-10 IL1ra 79 64 246 0.94 SAA IP-10 IL1ra 80 87 284 0.90 TRAILIP-10 IL1ra 81 3 30 0.72 SAA PCT IL1ra 82 6 33 0.67 TRAIL PCT IL1ra 8364 246 0.90 TRAIL SAA IL1ra 84 3 84 0.98 SAA PCT IP-10 85 6 100 0.68TRAIL PCT IP-10 86 81 493 0.96 TRAIL SAA IP-10 87 3 85 0.98 TRAIL SAAPCT 88 6 33 0.77 PCT IP-10 IL1ra CRP 89 64 245 0.95 SAA IP-10 IL1ra CRP90 87 283 0.95 TRAIL IP-10 IL1ra CRP 91 3 30 0.73 SAA PCT IL1ra CRP 92 633 0.74 TRAIL PCT IL1ra CRP 93 64 245 0.92 TRAIL SAA IL1ra CRP 94 3 840.98 SAA PCT IP-10 CRP 95 6 100 0.77 TRAIL PCT IP-10 CRP 96 78 490 0.97TRAIL SAA IP-10 CRP 97 3 85 0.80 TRAIL SAA PCT CRP 98 3 30 0.91 SAA PCTIP-10 IL1ra 99 6 33 0.67 TRAIL PCT IP-10 IL1ra 100 64 246 0.94 TRAIL SAAIP-10 IL1ra 101 3 30 0.78 TRAIL SAA PCT IL1ra 102 3 84 0.65 TRAIL SAAPCT IP-10 103 3 30 0.91 SAA PCT IP-10 IL1ra CRP 104 6 33 0.66 TRAIL PCTIP-10 IL1ra CRP 105 64 245 0.95 TRAIL SAA IP-10 IL1ra CRP 106 3 30 0.73TRAIL SAA PCT IL1ra CRP 107 3 84 0.97 TRAIL SAA PCT IP-10 CRP 108 3 300.78 TRAIL SAA PCT IP-10 IL1ra 109 3 30 0.73 TRAIL SAA PCT IP-10 IL1raCRP 110

Table 31 below list the coefficients b₀, b₁, b₂, etc that were used todefine the coordinate δ₁, for each of the 110 cases listed in Table 30,respectively. The first coefficient on the left is b₀, and then fromleft to right, the coefficients correspond to the order of thepolypeptides in each row of Table 30. The coefficients correspond to thefollowing concentration scales for each polypeptide: TRAIL (pg/ml),IP-10 (pg/ml), CRP (ug/ml), PCT (ng/ml), SAA (g/ml) and IL1ra (g/ml).

TABLE 31 Coefficients No. −9849178.8 1 −0.0009 2 0.6405 3 1098.3777 4−0.00089 5 4.5607 6 5283.68 7 −0.03151 8 0.86013 9 4677.8311 10 −0.028811 2349.8702 12 −0.019169 13 −0.02176 14 −0.00165 6.264E+7 15 1.07655−8.42E+7 16 2098.4 −2.22E+7 17 −0.0266 2.0497E+7  18 0.65349 −0.0005 191378.2 −0.00109 20 −1243.01 1.4735726 21 −0.010529 0.42793 22 −0.01891183.3117 23 4.8755 −0.001241 24 5777 −0.001377 25 −0.03151 −1.118−06 264823 2.91 27 −0.0342 1.941 28 −0.0264 3745.49 29 2427.6 1.3263344 30−0.020588 0.38993 31 −0.021174 3048.4182 32 −0.013629 1431.011 33 1.5−0.003888 75533424 34 2425.771 −0.002 59894763 35 −0.0251 −0.0008450294164 36 893.395 1.1316 −70994467 37 −0.0477 −0.084 −81575254 38−0.02483 1236 10145313 39 −949.2 1.528887 −5.5688E−4  40 −0.0111130.40033 0.00021523 41 −0.0177 329.7448 −0.0003975 42 −0.011 −1930.2 1.2443 6082.17 4.286 −0.002014 44 −0.0397 2.126 0.00092636 45 −0.02524082.939 −0.00062 46 −0.0560 7639.7 0.68134 47 −0.0134 1423.990.87764371 48 4736.86 1.250 −0.00652 172681901 49 −0.044 −0.121−0.000873 −4.62E+7 50 −0.0219 1576.6 −0.00134 54069432 51 −0.055 3598−0.098620 −74159142 52 −0.0116 −2055 1.188 0.00023 53 −0.055 8903.821.03 −0.0012627 54 −0.078 14133 −0.687 −0.009695 1.062E+8 55 3.996E+8 560.0063336 57 860.3249 58 9898.8177 59 0.00721 60 419.2 61 14320 620.00066 63 1089.4251 64 12590.5 65 −0.00905 66 165893.71 67 0.0041105 680.010541 69 0.0062 −77782071 70 393.7 559628637 71 8656.83 244256710 720.0129 157875482 73 846.608 0.0014 74 5900.1661 0.00927 75 131629 108.8476 0.011421 822.6365 77 0.013257 10662.5415 78 417.43 −0.000381 79 121280.0091619 80 −0.005459 0.007583 81 377360 −8.1908 82 0.00099 418.212 830.011194 17111.2 84 21649017 28.96307 85 0.00330 1086.1672 86−9.3941e−05 12572.6828 87 24.2 80696477 88 392.929 −0.0001 611767730 896854 0.00937 −157521601 90 0.005871 0.00552 −61236289 91 403954 −8.65767107285383 92 0.00857 373.75 383823513 93 0.013998 9692.125 −4665192.194 4998296 −132.70 0.3202 95 0.00927 827.6066 0.000498 96 0.003696461.9905 0.008696 97  2.32E+12 4.83248e+18  −1.05E+14 98 9471186 −2960.196688 99 0.002761 413.88 −0.00058 100 0.00349 12684.8 0.0088176 1011.3718 8853215 −272.0191 102 0.9352 11007611 24.21772 103 5448434 −1950.1975318 32157214873 104 0.024158 327.2 −0.002344 823767988 105 0.00657390.9 0.008791 −151905670 106 2.78 −1129873 −106.418 43593035460 1071.563 −96788.08 −22.217 0.4843 108    4.06E+12 1.757e+18    2.798E+13   3.97E+12 109 1.839 −9.83E+5 −16.687 0.58062 −4575512593 110Coefficients No. 0.0363 −1.997 1 0.039722 −1.6069 2 0.054137 −2.9681 30.034353 −2.33196 4 47954608.09 0.4715979 5 −69280395.624 −0.74822 6−33345728.8342 −1.706206 7 43833567.7377 3.0601663 8 −0.00060898−0.13268 9 −0.0009684361 −1.01872 10 0.00031349 2.5632 11 1.1895403−1.35195 12 0.4382 1.4742 13 2962.7685 1.08972 14 0.039986 −1.27532 150.0475326 −2.3376 16 0.027867 −2.23709 17 0.030146 0.9001561 18 0.051698−2.5383 19 0.034481544 −1.6940577 20 0.054245413 −2.7487888 21 0.04535−1.421 22 0.0312776 0.1044034 23 −4107077 −0.0013248 24 21179055−1.054077 25 43882108 3.0605 26 −68741718 −1.9806377 27 113905139.62.844483 28 −7296968.1 1.4399 29 −0.000765 −0.8562752 30 0.000453941.357 31 −0.000163 1.0917705 32 0.89320046 0.48274 33 0.07214 −0.6620 340.034006 −1.433018 35 0.03259 1.074937 36 0.038 −2.302 37 0.0616321.903272 38 0.025 0.65146 39 0.04984696 −2.32016 40 0.045264 −1.4662 410.03169 0.14333 42 0.050385 −1.109923 43 2715886 −1.087150 44 −15081202.9154 45 17100158 1.55114 46 −27909258 2.85226 47     6.13e−05 0.446 480.07676 −0.3021 49 0.0671 1.937 50 0.029267 0.78878 51 0.041577 2.309 520.0512 −1.1542 53 14035678 3.2 54 0.10 5.59 55 0.11089 −1.021759 560.11347 −1.9467 57 0.0639025 −84.98948 58 0.091563631 −0.3299621 59107920251.6624 −1.0006445 60 596535240 −41.585735 61 234257296.8937−0.4789050 62 812307573.5455 0.09918792 63 0.00069423293 −107.18015 640.00967490979 −2.05501 65 0.0092076 0.19189 66 122.7205081 −11.30895 676.5788 0.98581 68 19453.2163 −1.366750 69 0.10876 −2.301980 70 0.048935−39.915 71 0.0663 −0.885780 72 0.142003 −2.694252 73 0.07831107−84.66684 74 0.081369191 −2.5885198 75 0.06793071342 −10.12169 760.08303337 −82.88872 77 0.106214424 −2.33978 78 744190123.3893−41.369532 79 −130390666 −2.266204 80 82287681 −0.50010 81 6837963488−2.47028 82 560182293 −41.5502 83 29398797 −1.8577 84 0.4328 −156.16 850.00029753173 −107.01823 86 0.00969 −2.0464 87 471.6 −2614.99 88 0.0491−39.82 89 0.070555 −2.81351 90 0.118 −2.9416 91 −0.07356 −2.349 920.05763 −38.781 93 0.0965657 −2.782 94 10.567847038 −132.7427 950.08349426 −83.41464 96 0.084631596 −2.9639303 97 90376144988921.185E+14 98 116933544267 −99.64 99 713679677.7954 −41.177966 100−124943185 −2.6391378 101 68076716508 −163.16785 102 0.09197 −134.8402103 5.367 −82.7 104 0.0803 −35.325 105 0.080040 −3.579 106 29.2 −338.972107 8.2370 −237.8248 108 −5.96133e+22 −8.51E+14 109 9.549 −276.3 110

Two-Dimensional Manifold

When n=2, the manifold S is a curved surface and the hyperplane π is aflat plane defined by the first direction δ₀ and the second directionδ₁. The coordinate δ₀ in this Example is optionally and preferably alinear combination a₀+a₁D₁+a₂D₂+ . . . , of the polypeptides D₁, D₂,etc; and the coordinate δ₁ in this Example is optionally and preferablya linear combination b₀+b₁D₁+b₂D₂+ . . . , of the polypeptides D₁, D₂,etc.

Tables 32-35 below list diagnostic performance (in AUCs) attained forn=2. The performance were computed using a leave-10%-out crossvalidation on a subset of the majority cohort that had sufficient serumto measure all the proteins. The coordinates δ₀ and δ₁ were calculatedso that the probabilistic classification function f(δ₀,δ₁) representedthe likelihood that the test subject had a bacterial infection. The AUCvalues correspond to classifications according to Bacterial versus Viral(second column from right—B vs. V) and infectious vs. non-infectious(rightmost column—I vs. NI). Shown are results for the embodiments inwhich the plurality of polypeptides includes two polypeptides (Table32), three polypeptides (Table 33), four polypeptides (Table 34) andfive polypeptides (Table 35). The coefficients for the coordinates δ₀and δ₁ are presented for each polypeptide, wherein “const” correspond toa₀ when applied to the coordinate δ₀ and b₀ when applied to thecoordinate δ₁. The coefficients correspond to the followingconcentration scales for each protein: TRAIL (pg/ml), IP-10 (pg/ml), CRP(ug/ml), PCT (ng/ml), SAA (g/ml) and IL1ra (g/ml).

TABLE 32 AUC AUC (I vs. NI) (B vs. V) 0.91 0.88 TRAIL IP-10 Const 0.00060.0086 −0.3333 δ0 −0.0294 0.0089 2.4481 δ1 0.95 0.89 IP-10 CRP Const0.0055 0.0517 −0.474 δ0 0.0046 0.0902 −1.9201 δ1 0.96324 0.85647 SAAIP-10 Const 9623.7195 0.0089 −1.0634 δ0 14280.3897 0.0079 −2.0098 δ10.89408 0.63901 IP-10 IL1ra Const 0.0077 77589304.64 −0.2347 δ0 0.0069122880671.4 0.3245 δ1 0.735 0.70468 PCT IP-10 Const 0.1778 0.0012 1.3717δ0 0.9426 0.0007 1.3073 δ1 0.93 0.94 TRAIL CRP Const 0.0129 0.0647−0.551 δ0 −0.0077 0.0953 −0.1177 δ1 0.92719 0.90714 TRAIL SAA Const0.0146 15457.6689 −1.0101 δ0 −0.0081 18311.8735 0.2736 δ1 0.855230.88673 TRAIL IL1ra Const 0.0118 660539652.3 −0.1638 δ0 −0.0224691029794.9 3.3011 δ1 0.69731 0.86706 TRAIL PCT Const 0.0095 0.66990.7941 δ0 −0.0105 1.0871 2.4419 δ1 0.92 0.89 SAA CRP Const 7927.95780.0371 0.9937 δ0 9043.9184 0.0704 −1.2549 δ1 0.93 0.87 IL1ra CRP Const357544464 0.0549 0.9321 δ0 345095895 0.0895 −0.8849 δ1 0.85 0.88 PCT CRPConst 0.1493 0.0543 1.225 δ0 0.71 0.1052 −1.48 δ1 0.9154 0.82529 SAAIL1ra Const 11965 233885248 0.9453 δ0 17194.2625 201037678 −0.6599 δ10.84314 0.78722 SAA PCT Const 6627 −0.6192 1.4185 δ0 8964 0.2744 0.1417δ1 0.82323 0.58647 PCT IL1ra Const −1.0932 601268546 1.3547 δ0 0.7431600085479 0.7175 δ1

TABLE 33 AUC AUC (I vs. NI) (B vs. V) 0.96 0.94 TRAIL IP-10 CRP Const0.005 0.0053 0.0555 −1.0317 δ0 −0.0143 0.005 0.0884 −0.6693 δ1 0.96 0.91TRAIL SAA IP-10 Const 0.0047 9804.469 0.0087 −1.636  δ0 −0.016712810.9197 0.0085 −0.435  δ1 0.90 0.89 TRAIL IP-10 IL1ra Const 0.00560.0072 24233992.13 −0.7474 δ0 −0.0282 0.0073 57162308.55   2.6252 δ10.66 0.85 TRAIL PCT IP-10 Const 0.008 0.7463 0.0005 0.71  δ0 −0.01361.1103 0.001   2.1832 δ1 0.97318 0.91325 SAA IP-10 CRP Const 4964.90780.0079 0.0389 −1.1506 δ0 6345.7097 0.0069 0.0729 −2.7684 δ1 0.956950.90645 IP-10 IL1ra CRP Const 0.0062 −72572842.54 0.0635 −0.5109 δ00.0046 −16278785.64 0.1025 −1.6901 δ1 0.8 0.88475 PCT IP-10 CRP Const0.1083 0.0016 0.0598   0.1233 δ0 0.6599 0.0011 0.1081 −2.1504 δ1 0.949440.85722 SAA IP-10 IL1ra Const 9571.3145 0.0094 −141670519.4 −0.97  δ015309.775 0.008 −119518794.5 −1.932  δ1 0.95635 0.79658 SAA PCT IP-10Const 6137.1652 −0.6596 0.0047 −0.5085 δ0 8580.4524 0.2775 0.004 −1.3306δ1 0.73737 0.69549 PCT IP-10 IL1ra Const −1.1448 0.0005 540518195.3  1.0752 δ0 0.7431 −0.0003 578154355.6   0.9893 δ1 0.94489 0.93838 TRAILSAA CRP Const 0.0147 8741.563 0.0419 −1.1898 δ0 −0.0045 8922.431 0.0715−0.9205 δ1 0.92941 0.94316 TRAIL IL1ra CRP Const 0.0158 142723684.30.0735 −1.1214 δ0 −0.0124 142922206.2 0.1005 0.254 δ1 0.85644 0.91373TRAIL PCT CRP Const 0.0132 0.3236 0.066 −0.695  δ0 0.0019 0.6114 0.1084−1.7666 δ1 0.91298 0.91698 TRAIL SAA IL1ra Const 0.0165 13897.669319314215.49 −1.1796 δ0 −0.0114 17471.1789 −373899.4284   0.5955 δ10.9451 0.85 TRAIL SAA PCT Const 0.0281 13902.8636 −0.0348 −2.1844 δ00.0141 15302.3132 0.7361 −1.6348 δ1 0.73737 0.8797 TRAIL PCT IL1ra Const0.0126 1.6517 445497461.8 −0.3418 δ0 −0.0203 2.4203 638669048.4   2.2766δ1 0.91932 0.88856 SAA IL1ra CRP Const 7641.7563 224710899.2 0.0265  0.8638 δ0 9730.7248 201425116.6 0.0536 −1.256  δ1 0.90588 0.88556 SAAPCT CRP Const 8520.704 −1.4792 0.0207   1.1579 δ0 7599.3621 −0.22340.0695 −1.3994 δ1 0.84343 0.86842 PCT IL1ra CRP Const −0.6599547844063.4 0.0388   0.8368 δ0 −0.1506 473174484.1 0.0873 −1.6604 δ1 0.90.81448 SAA PCT IL1ra Const 10349.4815 −2.3088 565967860.9   1.0109 δ015172.8663 −0.2687 515166286.4 −1.0283 δ1

TABLE 34 AUC AUC (I vs. NI) (B vs. V) 0.97 0.94 TRAIL SAA IP-10 CRPConst 0.0058 5383.841 0.0075 0.0394 −1.7981 δ0 −0.012 5731.9467 0.007 0.0702 −1.5541 δ1 0.96 0.94 TRAIL IP-10 IL1ra CRP Const 0.0091 0.0053 −6.995E+7 0.0703 −1.5229 δ0 −0.0166 0.0046  −3.228E+7 0.101 −0.2128 δ10.78667 0.903 TRAIL PCT IP-10 CRP Const 0.0101 0.2921 0.0007 0.0651−0.6733 δ0 −0.0021 0.5293 0.001  0.1077 −1.8383 δ1 0.94957 0.91777 TRAILSAA IP-10 IL1ra Const 0.0091 10289.5699 0.0088 −153195983.2 −2.036  δ0−0.0169 14282.9357 0.0082 −138993063.2 −0.2825 δ1 0.93254 0.8433 TRAILSAA PCT IP-10 Const 0.0218 12161.0003 −0.2264   0.0068 −3.3387 δ0 0.008313578.3133 0.5366 0.0068 −2.9001 δ1 0.65657 0.86842 TRAIL PCT IP-10IL1ra Const 0.0147 1.6805 −0.0004   481673333.4 −0.356  δ0 −0.02682.4993 0.001  491494579.8   2.4805 δ1 0.95829 0.92002 SAA IP-10 IL1raCRP Const 6131.1692 0.0088 −1.5446E+8 0.028 −1.0249 δ0 8579.4749 0.0067−9.6352E+7 0.0614 −2.3655 δ1 0.9881 0.8735 SAA PCT IP-10 CRP Const4377.1407 −1.4641 0.0064 0.0419 −1.4913 δ0 3810.7522 −0.1982 0.00590.0857 −3.62  δ1 0.74242 0.89098 PCT IP-10 IL1ra CRP Const −0.48430.0004 4.54739E+8 0.0378   0.6379 δ0 −0.2044 −0.0018 4.84865E+8 0.0969−0.7642 δ1 0.94444 0.77828 SAA PCT IP-10 IL1ra Const 4951.1109 −2.82360.0095 −212692846.6 −0.802  δ0 10430.5725 −0.1446 0.008  −210027138.1−2.0339 δ1 0.92564 0.93742 TRAIL SAA IL1ra CRP Const 0.0163 8701.53992.10729E+7 0.0386 −1.3076 δ0 −0.0099 9890.6956 1.31614E+7 0.062 −0.2694δ1 0.95294 0.91111 TRAIL SAA PCT CRP Const 0.0253 11551.5028 −1.32850.0278 −1.8221 δ0 0.0141 9802.9581 −0.2648 0.0748 −2.7829 δ1 0.797980.89474 TRAIL PCT IL1ra CRP Const 0.0137 −0.1689  2.756E+8 0.0476−0.6344 δ0 −0.0264 −0.236  2.7563E+8 0.0994   0.5587 δ1 0.85556 0.92308TRAIL SAA PCT IL1ra Const 0.0343 12347.4916 −0.5098   432026875.9−2.2741 δ0 −0.0152 19586.5686 −0.4124   426850211.8   0.0383 δ1 0.90.85068 SAA PCT IL1ra CRP Const 2665.2949 −0.5099 6.42961E+8 0.0552  0.5611 δ0 3734.4091 −0.3614 5.88426E+8 0.0941 −1.8313 δ1

TABLE 35 AUC AUC (I vs. NI) (B vs. V) 0.95963 0.94381 TRAIL SAA IP-10IL1ra CRP Const 0.0092 6688.18   0.0082 −1.6265E+8 0.0336 −2.1333 δ0−0.0136 8261.93   0.0069 −1.17187E+8   0.0619 −1.1202 δ1 0.95635 0.89972TRAIL SAA PCT IP-10 CRP Const 0.0178 6302.89 −1.297  0.0074 0.0517−3.4117 δ0 0.0063 4437.96 −0.249  0.0076 0.1 −4.4957 δ1 0.71717 0.88346TRAIL PCT IP-10 IL1ra CRP Const 0.0246 −0.2302 −0.0017  5.4749E+8 0.0616−1.3864 δ0 −0.017 −0.2819 −0.0012  5.0261E+8 0.1096 −0.1627 δ1 0.855560.87783 TRAIL SAA PCT IP-10 IL1ra Const 0.0529 5922.72 −0.7334 0.01492530173.292 −6.1686 δ0 0.0043 14225.92 −0.282  0.0139 32115407.24−3.7073 δ1 0.91111 0.819 SAA PCT IP-10 IL1ra CRP Const −22863.96 −0.2611  0.0141 −8.7081E+8 0.1586 −2.8588 δ0 −18573.7 −0.3918 0.008 7.27742E+80.2362 −3.2596 δ1 0.87778 0.90045 TRAIL SAA PCT IL1ra CRP Const 0.0397−7661.57 −0.4075 6.98426E+8 0.1355 −3.522  δ0 −0.008 −4178.89 −0.49156.53495E+8 0.1689 −1.7514 δ1

Example 13 Exemplified Coordinates that Include Nonlinear Functions

It was unexpectedly found by the present Inventor that incorporation ofthe nonlinear functions ϕ₀ and ϕ₁ in the calculation of the coordinatesδ₁ and δ₂ captures more subtle trends in the data, while retaining aprobabilistic framework that allows meaningful interpretation of theresults. In this Example, the coordinates δ₀ and δ₁ were calculatedaccording to the following equations:

δ₀ =a ₀ +a ₁ C+a ₂ I+a ₃ T+ϕ ₀

δ₁ =b ₀ +b ₁ C+b ₂ I+b ₃ T+ϕ ₁,

and the nonlinear functions were defined as:

ϕ₀ =q ₁ C ^(γ1) +q ₂ C ^(γ2) +q ₃ T ^(γ3)

ϕ₁ =r ₁ C ^(γ1) +r ₂ C ^(γ2) +r ₃ T ^(γ3).

where γ1=0.5, γ2=2 and γ3=0.5.

Table 36 details the coefficients and constants used in this Example.

TABLE 36 First Coordinate Second Coordinate δ₀ (viral) δ₁ (bacterial) a₀= −0.8388 b₀ = 5.5123 Const a₁ = −0.0487 b₁ = −0.0636 CRP (mg/ml) q₁ =1.1367 r₁ = 1.4877 CRP^(0.5) (mg/ml)^(0.5) q₂ = −5.14 × 10⁻⁰⁵ r₂ = 3.50× 10⁻⁰⁵ CRP² (mg/ml)² a₂ = 0.0089 b₂ = 0.0085 IP10 (pg/ml) a₃ = 0.0408b₃ = 0.0646 TRAIL (pg/ml) q₃ = −0.6064 r₃ = −1.8039 TRAIL^(0.5)(pg/ml)^(0.5)

The performance of the model presented in Table 36 was examined on theMicrobiologically Confirmed Cohort (AUC of 0.95±0.03), Unanimous Cohort(AUC of 0.95±0.02) and the Study cohort (AUC of 0.93±0.02). Thesignature performance improved as the size of the equivocal regionincreases.

Tables 37A-C below detail signature measures of accuracy for diagnosingbacterial versus viral infections when using the nonlinear model of thepresent Example. Performance estimates and their 95% CIs were obtainedon the Microbiologically Confirmed sub-cohort (Table 37A; n=241),Unanimous sub-cohort (Table 37B; n=527), and Study Cohort (Table 37C;n=653), using different sizes of equivocal regions as indicated. Tables37D-F below detail percentage of patients who had equivocal immuneresponse in the Study Cohort when applying different thresholds, andTables 37G-H below detail signature sensitivity and specificity whenapplying different equivocal immune response thresholds obtained on theStudy Cohort. In Tables 37D-H the leftmost columns represents a minimalequivocal immune response threshold and the uppermost row represents amaximal equivocal immune response threshold.

TABLE 37A Equivocal Equivocal Equivocal Equivocal immune immune immuneimmune response response response response All Accuracy filter (10-90)filter (20-80) filter (30-70) filter (35-65) patients measure 0.98,0.96, 0.94, 0.93, 0.89, Total (0.96, 1.00) (0.93, 0.99) (0.91, 0.97)(0.90, 0.97) (0.85, 0.93) accuracy 0.96, 0.96, 0.95, 0.93, 0.88,Sensitivity (0.90, 1.00) (0.91, 1.00) (0.89, 1.00) (0.87, 1.00) (0.80,0.96) 0.99, 0.96, 0.94, 0.94, 0.90, Specificity (0.97, 1.00) (0.93,0.99) (0.90, 0.98) (0.90, 0.97) (0.87, 0.94) 65% 78% 87% 90% 100% % ofpatients included

TABLE 37B Equivocal Equivocal Equivocal Equivocal immune immune immuneimmune response response response response All Accuracy filter (10-90)filter (20-80) filter (30-70) filter (35-65) patients measure 0.97,0.95, 0.93, 0.92, 0.88, Total (0.95, 0.99) (0.93, 0.97) (0.90, 0.95)(0.89, 0.94) (0.85, 0.91) accuracy 0.96, 0.93, 0.91, 0.90, 0.85,Sensitivity (0.93, 0.99) (0.90, 0.97) (0.87, 0.95) (0.86, 0.94) (0.81,0.89) 0.98, 0.96, 0.94, 0.93, 0.91, Specificity (0.96, 1.00) (0.93,0.99) (0.91, 0.97) (0.90, 0.97) (0.87, 0.94) 63% 76% 86% 90% 100% % ofpatients included

TABLE 37C Equivocal Equivocal Equivocal Equivocal immune immune immuneimmune response response response response All Accuracy filter (10-90)filter (20-80) filter (30-70) filter (35-65) patients measure 0.95,0.92, 0.90, 0.89, 0.85, Total (0.93, 0.98) (0.90, 0.95) (0.87, 0.92)(0.86, 0.91) (0.83, 0.88) accuracy 0.95, 0.92, 0.89, 0.87, 0.83,Sensitivity (0.91, 0.98) (0.88, 0.95) (0.85, 0.92) (0.83, 0.91) (0.79,0.87) 0.95, 0.93, 0.91, 0.90, 0.87, Specificity (0.92, 0.98) (0.89,0.96) (0.88, 0.95) (0.87, 0.94) (0.84, 0.91) 58% 72% 84% 88% 100% % ofpatients included

TABLE 37D 0.95 0.9 0.85 0.8 0.75 0.7 0.65 0.6 0.55 0.5 0.45 0.4 0.35 0.30.25 0.2 0.15 0.1 52.8 47.2 44.0 40.9 38.6 36.3 34.8 33.2 31.2 29.1 26.324.0 22.7 20.5 17.6 13.9 10.4 6.6 0.05 46.2 40.6 37.4 34.3 32.0 29.728.2 26.6 24.7 22.5 19.8 17.5 16.1 13.9 11.0 7.4 3.8 0.1 42.4 36.8 33.530.5 28.2 25.9 24.3 22.8 20.8 18.7 15.9 13.6 12.3 10.1 7.2 3.5 0.15 38.933.2 30.0 27.0 24.7 22.4 20.8 19.3 17.3 15.2 12.4 10.1 8.7 6.6 3.7 0.235.2 29.6 26.3 23.3 21.0 18.7 17.2 15.6 13.6 11.5 8.7 6.4 5.1 2.9 0.2532.3 26.6 23.4 20.4 18.1 15.8 14.2 12.7 10.7 8.6 5.8 3.5 2.1 0.3 30.224.5 21.3 18.2 15.9 13.6 12.1 10.6 8.6 6.4 3.7 1.4 0.35 28.8 23.1 19.916.8 14.5 12.3 10.7 9.2 7.2 5.1 2.3 0.4 26.5 20.8 17.6 14.5 12.3 10.08.4 6.9 4.9 2.8 0.45 23.7 18.1 14.9 11.8 9.5 7.2 5.7 4.1 2.1 0.5 21.615.9 12.7 9.6 7.4 5.1 3.5 2.0 0.55 19.6 13.9 10.7 7.7 5.4 3.1 1.5 0.618.1 12.4 9.2 6.1 3.8 1.5 0.65 16.5 10.9 7.7 4.6 2.3 0.7 14.2 8.6 5.42.3 0.75 11.9 6.3 3.1 0.8 8.9 3.2 0.85 5.7 0.9

TABLE 37E 0.95 0.9 0.85 0.8 0.75 0.7 0.65 0.6 0.55 0.5 0.45 0.4 0.35 0.30.25 0.2 0.15 0.1 53.6 43.6 38.2 33.5 29.5 26.0 23.5 21.6 18.8 16.6 13.811.3 10.3 9.1 7.5 5.3 3.4 2.5 0.05 51.1 41.1 35.7 31.0 27.0 23.5 21.019.1 16.3 14.1 11.3 8.8 7.8 6.6 5.0 2.8 0.9 0.1 50.2 40.1 34.8 30.1 26.022.6 20.1 18.2 15.4 13.2 10.3 7.8 6.9 5.6 4.1 1.9 0.15 48.3 38.2 32.928.2 24.1 20.7 18.2 16.3 13.5 11.3 8.5 6.0 5.0 3.8 2.2 0.2 46.1 36.130.7 26.0 21.9 18.5 16.0 14.1 11.3 9.1 6.3 3.8 2.8 1.6 0.25 44.5 34.529.2 24.5 20.4 16.9 14.4 12.5 9.7 7.5 4.7 2.2 1.3 0.3 43.3 33.2 27.923.2 19.1 15.7 13.2 11.3 8.5 6.3 3.4 0.9 0.35 42.3 32.3 27.0 22.3 18.214.7 12.2 10.3 7.5 5.3 2.5 0.4 39.8 29.8 24.5 19.7 15.7 12.2 9.7 7.8 5.02.8 0.45 37.0 27.0 21.6 16.9 12.9 9.4 6.9 5.0 2.2 0.5 34.8 24.8 19.414.7 10.7 7.2 4.7 2.8 0.55 32.0 21.9 16.6 11.9 7.8 4.4 1.9 0.6 30.1 20.114.7 10.0 6.0 2.5 0.65 27.6 17.6 12.2 7.5 3.4 0.7 24.1 14.1 8.8 4.1 0.7520.1 10.0 4.7 0.8 15.4 5.3 0.85 10.0 0.9

TABLE 37F 0.95 0.9 0.85 0.8 0.75 0.7 0.65 0.6 0.55 0.5 0.45 0.4 0.35 0.30.25 0.2 0.15 0.1 52.1 50.6 49.4 47.9 47.3 46.1 45.5 44.3 43.1 41.0 38.336.2 34.4 31.4 27.2 22.2 17.1 10.5 0.05 41.6 40.1 38.9 37.4 36.8 35.635.0 33.8 32.6 30.5 27.8 25.7 24.0 21.0 16.8 11.7 6.6 0.1 35.0 33.5 32.330.8 30.2 29.0 28.4 27.2 26.0 24.0 21.3 19.2 17.4 14.4 10.2 5.1 0.1529.9 28.4 27.2 25.7 25.1 24.0 23.4 22.2 21.0 18.9 16.2 14.1 12.3 9.3 5.10.2 24.9 23.4 22.2 20.7 20.1 18.9 18.3 17.1 15.9 13.8 11.1 9.0 7.2 4.20.25 20.7 19.2 18.0 16.5 15.9 14.7 14.1 12.9 11.7 9.6 6.9 4.8 3.0 0.317.7 16.2 15.0 13.5 12.9 11.7 11.1 9.9 8.7 6.6 3.9 1.8 0.35 15.9 14.413.2 11.7 11.1 9.9 9.3 8.1 6.9 4.8 2.1 0.4 13.8 12.3 11.1 9.6 9.0 7.87.2 6.0 4.8 2.7 0.45 11.1 9.6 8.4 6.9 6.3 5.1 4.5 3.3 2.1 0.5 9.0 7.56.3 4.8 4.2 3.0 2.4 1.2 0.55 7.8 6.3 5.1 3.6 3.0 1.8 1.2 0.6 6.6 5.1 3.92.4 1.8 0.6 0.65 6.0 4.5 3.3 1.8 1.2 0.7 4.8 3.3 2.1 0.6 0.75 4.2 2.71.5 0.8 2.7 1.2 0.85 1.5 0.9

TABLE 37G 0.95 0.9 0.85 0.8 0.75 0.7 0.65 0.6 0.55 0.5 0.45 0.4 0.35 0.30.25 0.2 0.15 0.1 98.0 98.3 98.5 98.6 98.7 98.7 98.8 98.8 98.8 98.9 95.692.9 92.0 90.7 89.2 87.1 85.4 84.6 0.05 92.9 94.1 94.6 95.0 95.3 95.595.6 95.7 95.9 96.0 92.9 90.4 89.5 88.3 86.8 84.8 83.2 0.1 91.2 92.793.3 93.7 94.1 94.3 94.5 94.6 94.8 94.9 92.0 89.5 88.6 87.4 85.9 84.00.15 87.9 89.8 90.7 91.3 91.7 92.1 92.3 92.5 92.8 92.9 90.1 87.7 86.885.7 84.3 0.2 84.3 86.8 87.8 88.6 89.2 89.6 89.9 90.1 90.5 90.7 88.085.7 84.8 83.8 0.25 81.9 84.7 85.8 86.7 87.4 87.9 88.3 88.5 88.9 89.286.5 84.3 83.5 0.3 80.1 83.1 84.3 85.3 86.0 86.6 87.0 87.3 87.7 88.085.4 83.2 0.35 78.8 81.9 83.3 84.3 85.1 85.7 86.1 86.4 86.8 87.1 84.60.4 75.5 79.0 80.5 81.6 82.5 83.2 83.7 84.0 84.5 84.8 0.45 72.1 76.077.6 78.9 79.9 80.6 81.1 81.5 82.1 0.5 73.1 76.7 78.2 79.4 80.4 81.181.6 81.9 0.55 74.2 77.5 78.9 80.1 81.0 81.6 82.1 0.6 74.9 78.0 79.480.5 81.3 82.0 0.65 75.8 78.7 80.0 81.0 81.8 0.7 76.9 79.6 80.8 81.70.75 78.0 80.5 81.6 0.8 79.3 81.5 0.85 80.5 0.9

TABLE 37H 0.95 0.9 0.85 0.8 0.75 0.7 0.65 0.6 0.55 0.5 0.45 0.4 0.35 0.30.25 0.2 0.15 0.1 97.5 94.5 92.3 89.7 88.6 86.7 85.7 83.9 82.1 79.2 80.180.8 81.3 82.1 83.1 84.2 85.2 86.3 0.05 97.9 95.5 93.6 91.4 90.5 88.888.0 86.4 84.9 82.3 83.0 83.5 83.9 84.5 85.3 86.1 86.9 0.1 98.2 95.994.2 92.2 91.4 89.9 89.1 87.7 86.2 83.9 84.4 84.8 85.1 85.7 86.3 87.10.15 98.3 96.2 94.7 92.7 92.0 90.6 89.8 88.5 87.1 84.9 85.4 85.7 86.086.5 87.1 0.2 98.4 96.5 95.0 93.2 92.5 91.1 90.5 89.2 87.9 85.8 86.286.5 86.8 87.2 0.25 98.5 96.7 95.3 93.5 92.9 91.6 90.9 89.7 88.5 86.486.8 87.1 87.3 0.3 98.5 96.8 95.4 93.8 93.1 91.9 91.2 90.0 88.9 86.987.2 87.5 0.35 98.6 96.9 95.5 93.9 93.3 92.0 91.4 90.2 89.1 87.1 87.50.4 98.6 96.9 95.6 94.0 93.4 92.2 91.6 90.4 89.3 87.4 0.45 98.7 97.095.8 94.2 93.6 92.4 91.8 90.7 89.6 0.5 96.4 94.8 93.6 92.1 91.6 90.489.9 88.8 0.55 95.1 93.6 92.4 91.0 90.4 89.3 88.8 0.6 93.9 92.4 91.389.9 89.3 88.3 0.65 93.3 91.8 90.7 89.3 88.8 0.7 92.1 90.7 89.6 88.30.75 91.6 90.2 89.1 0.8 90.2 88.8 0.85 89.1 0.9

The signature performance was further examined on the Study Cohort whenexcluding the following two subgroups: (i) patients whose blood samplewas taken after more than 3 days of antibiotic treatment in the hospitaland (ii) patients with a suspected gastroenteritis. Details of the modelperformance on the Microbiologically Confirmed Cohort (AUC of0.96±0.04), Unanimous Cohort (AUC of 0.96±0.02) and the Study cohort(AUC of 0.95±0.02) is further depicted in Table 38A-C.

Tables 38A-C detail signature measures of accuracy for diagnosingbacterial vs. viral infections using the non-linear MLR model.Performance estimates and their 95% CIs were obtained on theMicrobiologically Confirmed sub-cohort (Table 38A; n=200), Unanimoussub-cohort (Table 38B; n=402), and Study Cohort (Table 38C; n=491), whenexcluding patients with over 3 days of antibiotics treatment at thehospital and/or suspicion of gastroenteritis.

TABLE 38A Equivocal Equivocal Equivocal Equivocal immune immune immuneimmune response response response response All Accuracy filter (10-90)filter (20-80) filter (30-70) filter (35-65) patients measure 0.98,0.96, 0.95, 0.95, 0.91, Total (0.96, 1) (0.93, 0.99) (0.92, 0.99)(0.92, 1) (0.86, 0.95) accuracy 0.94, 0.95, 0.96, 0.96, 0.90,Sensitivity (87, 1) (0.89, 1) (0.89, 1) (0.89, 1) (0.82, 0.99) 1, 0.97,0.95, 0.95, 0.91, Specificity (1, 1) (0.93, 1) (0.92, 0.99) (0.91, 0.99)(0.86, 0.95) 65% 80% 88% 90% 100% % of patients included

TABLE 38B Equivocal Equivocal Equivocal Equivocal immune immune immuneimmune response response response response All Accuracy filter (10-90)filter (20-80) filter (30-70) filter (35-65) patients measure 0.98,0.96, 0.95, 0.94, 0.91, Total (0.96, 1) (0.94, 0.98) (0.93, 0.97) (0.92,0.97) (0.88, 0.94) accuracy 0.98, 0.95, 0.94, 0.93, 0.89, Sensitivity(0.95, 1) (0.92, 0.99) (0.90, 0.98) (0.89, 0.97) (0.85, 0.94) 0.99,0.97, 0.95, 0.95, 0.92, Specificity (0.97, 1) (0.94, 0.99) (0.93, 0.98)(0.92, 0.98) (0.88, 0.96) 65% 79% 88% 91% 100% % of patients included

TABLE 38C Equivocal Equivocal Equivocal Equivocal immune immune immuneimmune response response response response All Accuracy filter (10-90)filter (20-80) filter (30-70) filter (35-65) patients measure 0.97,0.94, 0.93, 0.91, 0.88, Total (0.95, 0.99) (0.92, 0.97) (0.90, 0.95)(0.89, 0.94) (0.85, 0.91) accuracy 0.97, 0.95, 0.92, 0.91, 0.87,Sensitivity (0.94, 1) (0.91, 0.98) (0.88, 0.96) (0.87, 0.95) (0.83,0.92) 0.97, 0.94, 0.93, 0.92, 0.89, Specificity (0.94, 1) (0.91, 0.97)(0.90, 0.96) (0.89, 0.96) (0.85, 0.92) 59% 74% 85% 88% 100% % ofpatients included

Example 14 Antibiotics Based Stratification

Of the 653 patients with suspicion of acute infection, 427 receivedantibiotics (299 had bacterial diagnosis and 128 had viral diagnosis).The AUC of the signature for distinguishing between the bacterial andviral infected patients in the antibiotics treated patients sub-cohortwas 0.93±0.02. No statistically significant difference was observedbetween the performance on the antibiotics treated patients and thegeneral cohort (0.94±0.02 versus 0.93±0.02; P=0.5).

Although the invention has been described in conjunction with specificembodiments thereof, it is evident that many alternatives, modificationsand variations will be apparent to those skilled in the art.Accordingly, it is intended to embrace all such alternatives,modifications and variations that fall within the spirit and broad scopeof the appended claims.

It is the intent of the Applicant(s) that all publications, patents andpatent applications referred to in this specification are to beincorporated in their entirety by reference into the specification, asif each individual publication, patent or patent application wasspecifically and individually noted when referenced that it is to beincorporated herein by reference. In addition, citation oridentification of any reference in this application shall not beconstrued as an admission that such reference is available as prior artto the present invention. To the extent that section headings are used,they should not be construed as necessarily limiting. In addition, anypriority document(s) of this application is/are hereby incorporatedherein by reference in its/their entirety.

What is claimed is:
 1. A computer-implemented method for analyzingbiological data, the method comprising: displaying on a display device agraphical user interface (GUI) having a calculation activation control;receiving values of biomarkers in the blood of a subject; responsivelyto an activation of said control by a user, automatically calculating ascore based on said values; generating on said GUI a graphical scaleindicative of likelihoods for bacterial infections; and generating amark on said scale at a location corresponding to said score.
 2. Themethod of claim 1, wherein said scale has a first end identified ascorresponding to a higher likelihood for a non-bacterial infection ofsaid subject, and a second end identified as corresponding to a higherlikelihood for a bacterial infection.
 3. The method of claim 1, whereinsaid likelihoods are presented using a color index.
 4. The method ofclaim 1, wherein said receiving said values comprises communicating witha machine that measures said values.
 5. The method of claim 4, whereinsaid GUI further comprises a communication control, wherein saidcommunicating with said machine is in response to an activation of saidcommunication control by the user.
 6. The method of claim 1, whereinsaid GUI comprising a plurality of an value input fields, wherein saidreceiving said values is via said input fields.
 7. The method of claim6, wherein said GUI comprises a clearing control, and the methodcomprises clearing said value input fields responsively to an activationof said clearing control by said user.
 8. The method of claim 6,comprising clearing said value input automatically.
 9. The method ofclaim 1, comprising displaying said score numerically on said GUI. 10.The method according to claim 1, wherein said score is a likelihood thatthe subject has bacterial infection.
 11. The method according to claim1, wherein said score is a likelihood that the subject has viralinfection.
 12. The method according to claim 1, wherein said calculatingsaid score comprises calculating a distance between a segment of acurved surface and a plane defined by a first direction and a seconddirection, said distance being calculated at a point over said surfacedefined by first coordinate δ₀ along said first direction and a secondcoordinate δ₁ along said second direction; and using said distance forcalculation said score; wherein each of said coordinates is defined by adifferent combination of said values.
 13. The method according to claim12, wherein for at least one of said coordinates, said combination ofsaid values comprises a linear combination of said values.
 14. Themethod according to claim 13, wherein for at least one of saidcoordinates, said combination of the values includes at least onenonlinear term corresponding to at least one of said values.
 15. Themethod according to claim 12, comprising calculating an additionaldistance between a segment of an additional curved surface and saidplane, and using said additional distance for calculation said score.16. The method according to claim 15, comprising comparing each of saiddistance and said additional distance to a respective predeterminedthreshold, and, generating on said GUI an output indicative of saidcomparison.
 17. The method according to claim 1, comprising displayingon said GUI an output that summarizes scores calculated for previousblood samples.
 18. A system for analyzing biological data, the systemcomprising a display device and a data processor, wherein said dataprocessor is configured to display on said display device a graphicaluser interface (GUI) having a calculation activation control, to receivevalues biomarkers in the blood of a subject, to automatically calculatea score based on said values, responsively to an activation of saidcalculation activation control by a user, to generate on said GUI agraphical scale having a first end identified as corresponding to aviral infection of said subject, and a second end identified ascorresponding to a bacterial infection said subject, and to generate amark on said scale at a location corresponding to said score.
 19. Thesystem according to claim 18, wherein said data processor is configuredto communicate with a machine that measures said values, and to receivesaid values from said machine.
 20. The system according to claim 19,wherein said GUI further comprises a communication control, wherein saiddata processor is configured to communicate with said machine inresponse to an activation of said communication control by the user. 21.The system according to claim 18, wherein said GUI comprising aplurality of value input fields, and said data processor is configuredto receive said values is via said input fields.
 22. The systemaccording to claim 18, wherein said data processor is configured todisplay on said GUI an output that summarizes scores calculated forprevious blood samples.